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: "Reinforcement learning in robotics: A survey"
  Abstract: Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.

Connected through hyperedge HE-1 (induced by source paper "RobotIST: Interactive Situated Tangible Robot Programming"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Learning visual representations for perception-action systems"):
  with members:
    - "Symbolic Boolean manipulation with ordered binary-decision diagrams"

Connected through hyperedge HE-3 (induced by source paper "Cooperative and Competitive Reinforcement and Imitation Learning for a Mixture of Heterogeneous Learning Modules"):
  with members:
    - "Human-level control through deep reinforcement learning"

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