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: "DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model"
  Abstract: The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body part detectors that generate effective bottom-up proposals for body parts; (2) novel image-conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations; and (3) an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speed-up factors. Evaluation is done on two single-person and two multi-person pose estimation benchmarks. The proposed approach significantly outperforms best known multi-person pose estimation results while demonstrating competitive performance on the task of single person pose estimation (Models and code available at http://pose.mpi-inf.mpg.de).

Connected through hyperedge HE-1 (induced by source paper "Deeply-Supervised Nets"):
  with members:
    - "Deep multiple instance learning for image classification and auto-annotation"
    - "Maxout Networks"
    - "Densely Connected Convolutional Networks"
    - "Oriented Response Networks"

Connected through hyperedge HE-2 (induced by source paper "Toward safe separation distance monitoring from RGB-D sensors in human-robot interaction."):
  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