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 "Real-time human pose recognition in parts from single depth images"):
  Abstract: We propose a new method to quickly and accurately predict 3D positions of body joints from a single depth image, using no temporal information. We take an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler per-pixel classification problem. Our large and highly varied training dataset allows the classifier to estimate body parts invariant to pose, body shape, clothing, etc. Finally we generate confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes. The system runs at 200 frames per second on consumer hardware. Our evaluation shows high accuracy on both synthetic and real test sets, and investigates the effect of several training parameters. We achieve state of the art accuracy in our comparison with related work and demonstrate improved generalization over exact whole-skeleton nearest neighbor matching.

Includes members:
  Member "Multimodal Multipart Learning for Action Recognition in Depth Videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Learning Invariant Color Features for Person Reidentification")
  Member "Classifying and visualizing motion capture sequences using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks")
  Member "Efficient Model-based 3D Tracking of Hand Articulations using Kinect":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3D Tracking of Multiple Objects with Identical Appearance Using RGB-D Input")
  Member "Histogram of 3D Facets: A characteristic descriptor for hand gesture recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "American Sign Language alphabet recognition using Convolutional Neural Networks with multiview augmentation and inference fusion")
  Member "Optimal Decisions from Probabilistic Models: The Intersection-over-Union Case":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Perceptually Inspired Layout-Aware Losses for Image Segmentation")
  Member "Dynamic time warping for gesture-based user identification and authentication with Kinect":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Feature design scheme for Kinect-based DTW human gesture recognition")
  Member "Discriminative learning of visual words for 3D human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion")
  Member "Using Affect Awareness to Modulate Task Experience: A Study Amongst Pre-elementary School Kids":
    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