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: "Multimodal Multipart Learning for Action Recognition in Depth Videos"
  Abstract: The articulated and complex nature of human actions makes the task of action recognition difficult. One approach to handle this complexity is dividing it to the kinetics of body parts and analyzing the actions based on these partial descriptors. We propose a joint sparse regression based learning method which utilizes the structured sparsity to model each action as a combination of multimodal features from a sparse set of body parts. To represent dynamics and appearance of parts, we employ a heterogeneous set of depth and skeleton based features. The proper structure of multimodal multipart features are formulated into the learning framework via the proposed hierarchical mixed norm, to regularize the structured features of each part and to apply sparsity between them, in favor of a group feature selection. Our experimental results expose the effectiveness of the proposed learning method in which it outperforms other methods in all three tested datasets while saturating one of them by achieving perfect accuracy.

Connected through hyperedge HE-1 (induced by source paper "Real-time human pose recognition in parts from single depth images"):
  with members:
    - "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"
    - "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 "Learning Invariant Color Features for Person Reidentification"):
  with members:
    - "Group Re-Identification: Leveraging and Integrating Multi-Grain Information"

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