human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups authors who co-authored the same paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Do Deep Nets Really Need to be Deep"):
  Abstract: Currently, deep neural networks are the state of the art on problems such as speech recognition and computer vision. In this paper we empirically demonstrate that shallow feed-forward nets can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models. Moreover, in some cases the shallow nets can learn these deep functions using the same number of parameters as the original deep models. On the TIMIT phoneme recognition and CIFAR-10 image recognition tasks, shallow nets can be trained that perform similarly to complex, well-engineered, deeper convolutional models.

Includes members:
  Member "Maxout Networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Neural Machine Translation by Jointly Learning to Align and Translate")
  Member "Learning to Learn: Model Regression Networks for Easy Small Sample Learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Dynamic Conditional Networks for Few-Shot Learning")
  Member "The Loss Surface of Deep and Wide Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "On the Margin Theory of Feedforward Neural Networks")
  Member "Neural self-compressor: Collective interpretation by compressing multi-layered neural networks into non-layered networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Recurrent Models of Visual Attention")

Question: Please tell me which research area the co-authored paper (i.e., the source paper of the center hyperedge) belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science