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: "Learning visual representations for perception-action systems"
  Abstract: We discuss vision as a sensory modality for systems that interact flexibly with uncontrolled environments. Instead of trying to build a generic vision system that produces task-independent representations, we argue in favor of task-specific, learn-able representations. This concept is illustrated by two examples of our own work. First, our RLVC algorithm performs reinforcement learning directly on the visual input space. To make this very large space manageable, RLVC interleaves the reinforcement learner with a supervised classification algorithm that seeks to split perceptual states so as to reduce perceptual aliasing. This results in an adaptive discretization of the perceptual space based on the presence or absence of visual features. Its extension, RLJC, additionally handles continuous action spaces. In contrast to the minimalistic visual representations produced by RLVC and RLJC, our second method learns structural object models for robust object detection and pose estimation by probabilistic inference. To these models, the method associates grasp experiences autonomously learned by trial and error. These experiences form a non-parametric representation of grasp success likelihoods over gripper poses, which we call a grasp density. Thus, object detection in a novel scene simultaneously produces suitable grasping options.

Connected through hyperedge HE-1 (induced by source paper "Reinforcement learning in robotics: A survey"):
  with members:
    - "Cooperative and Competitive Reinforcement and Imitation Learning for a Mixture of Heterogeneous Learning Modules"
    - "RobotIST: Interactive Situated Tangible Robot Programming"

Connected through hyperedge HE-2 (induced by source paper "Symbolic Boolean manipulation with ordered binary-decision diagrams"):
  with members:
    - "Planning via Model Checking: A Decision Procedure for AR"
    - "Symbolic protocol verification with queue BDDs"
    - "Deriving Petri nets from finite transition systems"
    - "Using BDDs to verify multipliers"
    - "FPGA routing and routability estimation via Boolean satisfiability"

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