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: "Densely Connected Convolutional Networks"
  Abstract: Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet.

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"
    - "Do Deep Nets Really Need to be Deep"
    - "Deep image mining for diabetic retinopathy screening"
    - "Adversarial Examples - A Complete Characterisation of the Phenomenon"

Connected through hyperedge HE-2 (induced by source paper "Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology"):
  with members:
    - "Scalable Person Re-identification: A Benchmark"

Connected through hyperedge HE-3 (induced by source paper "Deep Sparse Rectifier Neural Networks"):
  with members:
    - "Opinion Mining and Sentiment Analysis"
    - "DeepNAT: Deep convolutional neural network for segmenting neuroanatomy"
    - "Maxout Networks"
    - "DAU-GAN: Unsupervised Object Transfiguration via Deep Attention Unit"
    - "WiDet: Wi-Fi Based Device-Free Passive Person Detection with Deep Convolutional Neural Networks"

Connected through hyperedge HE-4 (induced by source paper "Deeply-Supervised Nets"):
  with members:
    - "Deep multiple instance learning for image classification and auto-annotation"
    - "Maxout Networks"
    - "DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model"
    - "Oriented Response Networks"

Connected through hyperedge HE-5 (induced by source paper "Graph Convolutional Reinforcement Learning for Multi-Agent Cooperation."):
  with members:
    - "Human-level control through deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "On Calibration of Modern Neural Networks."):
  with members:
    - "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank"
    - "Semantic Reconstruction: Reconstruction of Semantically Segmented 3D Meshes via Volumetric Semantic Fusion"

Connected through hyperedge HE-7 (induced by source paper "Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds"):
  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