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 with Factored States and Actions"
  Abstract: A novel approximation method is presented for approximating the value function and selecting good actions for Markov decision processes with large state and action spaces. The method approximates state-action values as negative free energies in an undirected graphical model called a product of experts. The model parameters can be learned efficiently because values and derivatives can be efficiently computed for a product of experts. Actions can be found even in large factored action spaces by the use of Markov chain Monte Carlo sampling. Simulation results show that the product of experts approximation can be used to solve large problems. In one simulation it is used to find actions in action spaces of size 240.

Connected through hyperedge HE-1 (induced by source paper "Vector-space analysis of belief-state approximation for POMDPs"):
  with members:
    - "Value-Directed Sampling Methods for POMDPs"

Connected through hyperedge HE-2 (induced by source paper "Exploiting structure and utilizing agent-centric rewards to promote coordination in large multiagent systems"):
  with members:
    (none)

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: Mathematics