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: "Classifying and visualizing motion capture sequences using deep neural networks"
  Abstract: The gesture recognition using motion capture data and depth sensors has recently drawn more attention in vision recognition. Currently most systems only classify dataset with a couple of dozens different actions. Moreover, feature extraction from the data is often computational complex. In this paper, we propose a novel system to recognize the actions from skeleton data with simple, but effective, features using deep neural networks. Features are extracted for each frame based on the relative positions of joints (PO), temporal differences (TD), and normalized trajectories of motion (NT). Given these features a hybrid multi-layer perceptron is trained, which simultaneously classifies and reconstructs input data. We use deep autoencoder to visualize learnt features. The experiments show that deep neural networks can capture more discriminative information than, for instance, principal component analysis can. We test our system on a public database with 65 classes and more than 2,000 motion sequences. We obtain an accuracy above 95% which is, to our knowledge, the state of the art result for such a large dataset.

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"
    - "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"
    - "Discriminative learning of visual words for 3D human pose estimation"
    - "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 "Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks"):
  with members:
    - "Deformable Pose Traversal Convolution for 3D Action and Gesture Recognition"

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