human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"):
  Abstract: In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games to a broader framework. We design a multiagent Q-learning method under this framework, and prove that it converges to a Nash equilibrium under specified conditions. This algorithm is useful for finding the optimal strategy when there exists a unique Nash equilibrium in the game. When there exist multiple Nash equilibria in the game, this algorithm should be combined with other learning techniques to find optimal strategies.

Includes members:
  Member "Markov games as a framework for multi-agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems")
  Member "A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms")
  Member "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Adaptive Load Balancing: A Study in Multi-Agent Learning")
  Member "Reinforcement Learning: A Survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems")
  Member "Learning Roles: Behavioral Diversity in Robot Teams":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Clay: Integrating Motor Schemas and Reinforcement Learning")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Reinforcement_Learning