Improve DANI dataset card: Add task category, library, tags, paper, and code links

This PR enhances the DANI dataset card by:
- Adding `task_categories: image-classification` to the metadata, aligning with the dataset's core purpose of distinguishing AI-generated from natural images.
- Including `library_name: datasets` to indicate the primary library for loading the dataset.
- Adding relevant `tags` such as `ai-generated-content`, `image-quality-assessment`, `real-vs-fake`, and `multimodal` for better discoverability.
- Integrating direct links to the paper ([D-Judge: How Far Are We? Evaluating the Discrepancies Between AI-synthesized Images and Natural Images through Multimodal Guidance](https://huggingface.co/papers/2412.17632)) and the associated GitHub repository ([https://github.com/RenyangLiu/DJudge](https://github.com/RenyangLiu/DJudge)) at the top of the card for quick access.

README.md

@@ -1,9 +1,20 @@
 ---
 license: cc-by-nc-4.0
+task_categories:
+  - image-classification
+library_name: datasets
+tags:
+  - ai-generated-content
+  - image-quality-assessment
+  - real-vs-fake
+  - multimodal
 ---
 
 # DANI: Discrepancy Accessing for Natural and AI Images
 
+[Paper: D-Judge: How Far Are We? Evaluating the Discrepancies Between AI-synthesized Images and Natural Images through Multimodal Guidance](https://huggingface.co/papers/2412.17632)
+[Code: https://github.com/RenyangLiu/DJudge](https://github.com/RenyangLiu/DJudge)
+
 **A Large-Scale Dataset for Visual Research on AI-Synthesized and Natural Images**
 
 ## Overview