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: "Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis."
  Abstract: Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods which can learn with less/other types of supervision, have been proposed. We review semi-supervised, multiple instance, and transfer learning in medical imaging, both in diagnosis/detection or segmentation tasks. We also discuss connections between these learning scenarios, and opportunities for future research.

Connected through hyperedge HE-1 (induced by source paper "Cats or CAT scans: transfer learning from natural or medical image source datasets?."):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition"):
  with members:
    - "Deep multiple instance learning for image classification and auto-annotation"

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