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: "Deep multiple instance learning for image classification and auto-annotation"
  Abstract: The recent development in learning deep representations has demonstrated its wide applications in traditional vision tasks like classification and detection. However, there has been little investigation on how we could build up a deep learning framework in a weakly supervised setting. In this paper, we attempt to model deep learning in a weakly supervised learning (multiple instance learning) framework. In our setting, each image follows a dual multi-instance assumption, where its object proposals and possible text annotations can be regarded as two instance sets. We thus design effective systems to exploit the MIL property with deep learning strategies from the two ends; we also try to jointly learn the relationship between object and annotation proposals. We conduct extensive experiments and prove that our weakly supervised deep learning framework not only achieves convincing performance in vision tasks including classification and image annotation, but also extracts reasonable region-keyword pairs with little supervision, on both widely used benchmarks like PASCAL VOC and MIT Indoor Scene 67, and also a dataset for image-and patch-level annotations.

Connected through hyperedge HE-1 (induced by source paper "Deeply-Supervised Nets"):
  with members:
    - "Maxout Networks"
    - "Densely Connected Convolutional Networks"
    - "DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model"
    - "Oriented Response Networks"

Connected through hyperedge HE-2 (induced by source paper "FALCON: A Fast Drop-In Replacement of Citation KNN for Multiple Instance Learning"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition"):
  with members:
    - "Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis."

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