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: "Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds"
  Abstract: In this paper, we present a deep learning architecture which addresses the problem of 3D semantic segmentation of unstructured point clouds. Compared to previous work, we introduce grouping techniques which define point neighborhoods in the initial world space and the learned feature space. Neighborhoods are important as they allow to compute local or global point features depending on the spatial extend of the neighborhood. Additionally, we incorporate dedicated loss functions to further structure the learned point feature space: the pairwise distance loss and the centroid loss. We show how to apply these mechanisms to the task of 3D semantic segmentation of point clouds and report state-of-the-art performance on indoor and outdoor datasets.

Connected through hyperedge HE-1 (induced by source paper "Densely Connected Convolutional Networks"):
  with members:
    - "Deeply-Supervised Nets"
    - "Deep Sparse Rectifier Neural Networks"
    - "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."
    - "How to train your MAML."

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