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: "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.

Connected through hyperedge HE-1 (induced by source paper "Histogram of 3D Facets: A characteristic descriptor for hand gesture recognition"):
  with members:
    - "American Sign Language alphabet recognition using Convolutional Neural Networks with multiview augmentation and inference fusion"

Connected through hyperedge HE-2 (induced by source paper "Discriminative learning of visual words for 3D human pose estimation"):
  with members:
    - "HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion"
    - "Letters: A unified supervised codebook learning framework for classification"
    - "Object categorization based on a supervised mean shift algorithm"

Connected through hyperedge HE-3 (induced by source paper "Classifying and visualizing motion capture sequences using deep neural networks"):
  with members:
    - "Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks"

Connected through hyperedge HE-4 (induced by source paper "Efficient Model-based 3D Tracking of Hand Articulations using Kinect"):
  with members:
    - "3D Tracking of Multiple Objects with Identical Appearance Using RGB-D Input"
    - "Depth-Based Real-Time Hand Tracking with Occlusion Handling Using Kalman Filter and DAM-Shift"
    - "Characterizing Predicate Arity and Spatial Structure for Inductive Learning of Game Rules"
    - "Multi-scale Deep Learning for Gesture Detection and Localization"

Connected through hyperedge HE-5 (induced by source paper "Dynamic time warping for gesture-based user identification and authentication with Kinect"):
  with members:
    - "Soli: ubiquitous gesture sensing with millimeter wave radar"
    - "Feature design scheme for Kinect-based DTW human gesture recognition"

Connected through hyperedge HE-6 (induced by source paper "Optimal Decisions from Probabilistic Models: The Intersection-over-Union Case"):
  with members:
    - "Perceptually Inspired Layout-Aware Losses for Image Segmentation"

Connected through hyperedge HE-7 (induced by source paper "Using Affect Awareness to Modulate Task Experience: A Study Amongst Pre-elementary School Kids"):
  with members:
    (none)

Connected through hyperedge HE-8 (induced by source paper "Multimodal Multipart Learning for Action Recognition in Depth Videos"):
  with members:
    - "Learning Invariant Color Features for Person Reidentification"

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