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 Sparse Rectifier Neural Networks"
  Abstract: While logistic sigmoid neurons are more biologically plausible than hyperbolic tangent neurons, the latter work better for training multi-layer neural networks. This paper shows that rectifying neurons are an even better model of biological neurons and yield equal or better performance than hyperbolic tangent networks in spite of the hard non-linearity and non-dierentiabil ity

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"
    - "Do Deep Nets Really Need to be Deep"
    - "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 "Densely Connected Convolutional Networks"):
  with members:
    - "Deeply-Supervised Nets"
    - "Maxout Networks"
    - "Graph Convolutional Reinforcement Learning for Multi-Agent Cooperation."
    - "Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology"
    - "On Calibration of Modern Neural Networks."
    - "Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds"
    - "How to train your MAML."

Connected through hyperedge HE-3 (induced by source paper "Opinion Mining and Sentiment Analysis"):
  with members:
    - "Using Universal Linguistic Knowledge to Guide Grammar Induction"
    - "Opinion Mining and Information Fusion"
    - "Sentiment analysis in Facebook and its application to e-learning"
    - "Utility scoring of product reviews"
    - "Opinion observer: analyzing and comparing opinions on the Web"
    - "Mining newsgroups using networks arising from social behavior"
    - "User-level sentiment analysis incorporating social networks"
    - "Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior"

Connected through hyperedge HE-4 (induced by source paper "DeepNAT: Deep convolutional neural network for segmenting neuroanatomy"):
  with members:
    - "Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey"
    - "CT image segmentation of bone for medical additive manufacturing using a convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "DAU-GAN: Unsupervised Object Transfiguration via Deep Attention Unit"):
  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