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: "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.

Connected through hyperedge HE-1 (induced by source paper "Real-time human pose recognition in parts from single depth images"):
  with members:
    - "Multimodal Multipart Learning for Action Recognition in Depth Videos"
    - "Classifying and visualizing motion capture sequences using deep neural networks"
    - "Efficient Model-based 3D Tracking of Hand Articulations using Kinect"
    - "Histogram of 3D Facets: A characteristic descriptor for hand gesture recognition"
    - "Optimal Decisions from Probabilistic Models: The Intersection-over-Union Case"
    - "Dynamic time warping for gesture-based user identification and authentication with Kinect"
    - "Using Affect Awareness to Modulate Task Experience: A Study Amongst Pre-elementary School Kids"
    - "Action recognition robust to background clutter by using stereo vision"

Connected through hyperedge HE-2 (induced by source paper "HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion"):
  with members:
    - "Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey"
    - "Tracking People on a Torus"
    - "Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation"

Connected through hyperedge HE-3 (induced by source paper "Letters: A unified supervised codebook learning framework for classification"):
  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