human: Given a node-centered hypergraph: <hypergraph>, where nodes represent authors and hyperedges represent groups of authors who co-authored the same paper.

Details of the hypergraph:
Center node: "Cross Modal Distillation for Supervision Transfer"
  Abstract: In this work we propose a technique that transfers supervision between images from different modalities. We use learned representations from a large labeled modality as supervisory signal for training representations for a new unlabeled paired modality. Our method enables learning of rich representations for unlabeled modalities and can be used as a pre-training procedure for new modalities with limited labeled data. We transfer supervision from labeled RGB images to unlabeled depth and optical flow images and demonstrate large improvements for both these cross modal supervision transfers.

Connected through hyperedge HE-1 (induced by source paper "Viewpoints and keypoints"):
  with members:
    - "Viewpoint-aware object detection and pose estimation"

Connected through hyperedge HE-2 (induced by source paper "Learning to Learn: Model Regression Networks for Easy Small Sample Learning"):
  with members:
    - "Do Deep Nets Really Need to be Deep"
    - "Dynamic Conditional Networks for Few-Shot Learning"

Connected through hyperedge HE-3 (induced by source paper "Selective Distillation of Weakly Annotated GTD for Vision-based Slab Identification System."):
  with members:
    (none)

Question: Please tell me which research area the center author belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science