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: "Deformable Pose Traversal Convolution for 3D Action and Gesture Recognition"
  Abstract: The representation of 3D pose plays a critical role for 3D action and gesture recognition. Rather than representing a 3D pose directly by its joint locations, in this paper, we propose a Deformable Pose Traversal Convolution Network that applies one-dimensional convolution to traverse the 3D pose for its representation. Instead of fixing the receptive field when performing traversal convolution, it optimizes the convolution kernel for each joint, by considering contextual joints with various weights. This deformable convolution better utilizes the contextual joints for action and gesture recognition and is more robust to noisy joints. Moreover, by feeding the learned pose feature to a LSTM, we perform end-to-end training that jointly optimizes 3D pose representation and temporal sequence recognition. Experiments on three benchmark datasets validate the competitive performance of our proposed method, as well as its efficiency and robustness to handle noisy joints of pose.

Connected through hyperedge HE-1 (induced by source paper "Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks"):
  with members:
    - "Classifying and visualizing motion capture sequences using deep neural networks"

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