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 "Discriminative learning of visual words for 3D human pose estimation"):
  Abstract: This paper addresses the problem of recovering 3D human pose from a single monocular image, using a discriminative bag-of-words approach. In previous work, the visual words are learned by unsupervised clustering algorithms. They capture the most common patterns and are good features for coarse-grain recognition tasks like object classification. But for those tasks which deal with subtle differences such as pose estimation, such representation may lack the needed discriminative power. In this paper, we propose to jointly learn the visual words and the pose regressors in a supervised manner. More specifically, we learn an individual distance metric for each visual word to optimize the pose estimation performance. The learned metrics rescale the visual words to suppress unimportant dimensions such as those corresponding to background. Another contribution is that we design an appearance and position context (APC) local descriptor that achieves both selectivity and invariance while requiring no background subtraction. We test our approach on both a quasi-synthetic dataset and a real dataset (HumanEva) to verify its effectiveness. Our approach also achieves fast computational speed thanks to the integral histograms used in APC descriptor extraction and fast inference of pose regressors.

Includes members:
  Member "Real-time human pose recognition in parts from single depth images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Histogram of 3D Facets: A characteristic descriptor for hand gesture recognition")
  Member "HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Tracking People on a Torus")
  Member "Letters: A unified supervised codebook learning framework for classification":
    also connected through other hyperedges:
      (none)
  Member "Object categorization based on a supervised mean shift algorithm":
    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