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: "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.

Connected through hyperedge HE-1 (induced by source paper "Maxout Networks"):
  with members:
    - "Neural Machine Translation by Jointly Learning to Align and Translate"
    - "Deeply-Supervised Nets"
    - "Deep Sparse Rectifier Neural Networks"
    - "Densely Connected Convolutional Networks"
    - "Deep image mining for diabetic retinopathy screening"
    - "Adversarial Examples - A Complete Characterisation of the Phenomenon"

Connected through hyperedge HE-2 (induced by source paper "Learning to Learn: Model Regression Networks for Easy Small Sample Learning"):
  with members:
    - "Cross Modal Distillation for Supervision Transfer"
    - "Dynamic Conditional Networks for Few-Shot Learning"

Connected through hyperedge HE-3 (induced by source paper "Neural self-compressor: Collective interpretation by compressing multi-layered neural networks into non-layered networks"):
  with members:
    - "Recurrent Models of Visual Attention"

Connected through hyperedge HE-4 (induced by source paper "The Loss Surface of Deep and Wide Neural Networks"):
  with members:
    - "On the Margin Theory of Feedforward Neural Networks"
    - "On the Convergence Rate of Training Recurrent Neural Networks."

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