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 "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.

Includes members:
  Member "Deeply-Supervised Nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Maxout Networks")
  Member "Deep Sparse Rectifier Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Maxout Networks")
  Member "Maxout Networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Neural Machine Translation by Jointly Learning to Align and Translate")
  Member "Graph Convolutional Reinforcement Learning for Multi-Agent Cooperation.":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Human-level control through deep reinforcement learning")
  Member "Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Scalable Person Re-identification: A Benchmark")
  Member "On Calibration of Modern Neural Networks.":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Semantic Reconstruction: Reconstruction of Semantically Segmented 3D Meshes via Volumetric Semantic Fusion")
  Member "Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds":
    also connected through other hyperedges:
      (none)
  Member "How to train your MAML.":
    also connected through other hyperedges:
      (none)

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