human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Machine Learning,  Reinforcement Learning with Replacing Eligibility Traces"
    - "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"
    - "Multi-criteria reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "Reinforcement Learning: A Survey"):
  with members:
    - "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks"
    - "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems"
    - "Adding Memory to XCS"

Connected through hyperedge HE-3 (induced by source paper "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"):
  with members:
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "Reinforcement Learning in the Multi-Robot Domain"
    - "Learning in Multi-Robot Systems"
    - "Learning to coordinate without sharing information"
    - "Using Communication to Reduce Locality in Distributed Multi-Agent Learning"
    - "Adaptive Load Balancing: A Study in Multi-Agent Learning"
    - "Team-Partitioned, Opaque-Transition Reinforcement Learning"

Connected through hyperedge HE-4 (induced by source paper "Learning Roles: Behavioral Diversity in Robot Teams"):
  with members:
    - "Clay: Integrating Motor Schemas and Reinforcement Learning"

Connected through hyperedge HE-5 (induced by source paper "Markov games as a framework for multi-agent reinforcement learning"):
  with members:
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "A Competitive Approach to Game Learning"
    - "Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study"
    - "database"
    - "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"
    - "Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates"
    - "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems"
    - "Using Communication to Reduce Locality in Distributed Multi-Agent Learning"

Question: Please tell me which class the center node 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