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: "Markov games as a framework for multi-agent reinforcement learning"
  Abstract: In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsistic view, secondary agents can only be part of the environment and are therefore fixed in their behavior. The framework of Markov games allows us to widen this view to include multiple adaptive agents with interacting or competing goals. This paper considers a step in this direction in which exactly two agents with diametrically opposed goals share an environment. It describes a Q-learning-like algorithm for finding optimal policies and demonstrates its application to a simple two-player game in which the optimal policy is probabilistic.

Connected through hyperedge HE-1 (induced by source paper "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Reinforcement Learning: A Survey"
    - "Learning to coordinate without sharing information"

Connected through hyperedge HE-2 (induced by source paper "Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates"):
  with members:
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "A Tutorial on Learning With Bayesian Networks"

Connected through hyperedge HE-3 (induced by source paper "Team-Partitioned, Opaque-Transition Reinforcement Learning"):
  with members:
    - "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"
    - "Co-Evolving Soccer Softbot Team Coordination with Genetic Programming"
    - "INCREMENTAL SELF-IMPROVEMENT FOR LIFE-TIME MULTI-AGENT REINFORCEMENT LEARNING"

Connected through hyperedge HE-4 (induced by source paper "Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study"):
  with members:
    - "In  Improving Elevator Performance Using Reinforcement Learning"
    - "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997"

Connected through hyperedge HE-5 (induced by source paper "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"):
  with members:
    - "A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms"
    - "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"
    - "Reinforcement Learning: A Survey"
    - "Learning Roles: Behavioral Diversity in Robot Teams"

Connected through hyperedge HE-6 (induced by source paper "Figure 3: Average model size accepted from a ran-dom prefix-closed samples of various size, and"):
  with members:
    - "Learning to coordinate without sharing information"

Connected through hyperedge HE-7 (induced by source paper "database"):
  with members:
    - "Computing upper and lower bounds on likelihoods in intractable networks"
    - "Recursive algorithms for approximating probabilities in graphical models"

Connected through hyperedge HE-8 (induced by source paper "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"):
  with members:
    - "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"
    - "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"

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