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: "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"
  Abstract: Intelligent human agents exist in a cooperative social environment that facilitates learning. They learn not only by trial-and-error, but also through cooperation by sharing instantaneous information, episodic experience, and learned knowledge. The key investigations of this paper are, "Given the same number of reinforcement learning agents, will cooperative agents outperform independent agents who do not communicate during learning?" and "What is the price for such cooperation?" Using independent agents as a benchmark, cooperative agents are studied in following ways: (1) sharing sensation, (2) sharing episodes, and (3) sharing learned policies. This paper shows that (a) additional sensation from another agent is beneficial if it can be used efficiently, (b) sharing learned policies or episodes among agents speeds up learning at the cost of communication, and (c) for joint tasks, agents engaging in partnership can significantly outperform independent agents although they may learn slowly in the beginning. These tradeoffs are not just limited to multi-agent reinforcement learning.

Connected through hyperedge HE-1 (induced by source paper "Adaptive Load Balancing: A Study in Multi-Agent Learning"):
  with members:
    - "Learning to coordinate without sharing information"

Connected through hyperedge HE-2 (induced by source paper "Reinforcement Learning in the Multi-Robot Domain"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning"
    - "Using Communication to Reduce Locality in Distributed Multi-Agent Learning"

Connected through hyperedge HE-3 (induced by source paper "Learning to coordinate without sharing information"):
  with members:
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems"
    - "Adaptive Load Balancing: A Study in Multi-Agent Learning"
    - "Figure 3: Average model size accepted from a ran-dom prefix-closed samples of various size, and"

Connected through hyperedge HE-4 (induced by source paper "Team-Partitioned, Opaque-Transition Reinforcement Learning"):
  with members:
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "Co-Evolving Soccer Softbot Team Coordination with Genetic Programming"
    - "INCREMENTAL SELF-IMPROVEMENT FOR LIFE-TIME MULTI-AGENT REINFORCEMENT LEARNING"

Connected through hyperedge HE-5 (induced by source paper "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"):
  with members:
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms"
    - "Reinforcement Learning: A Survey"
    - "Learning Roles: Behavioral Diversity in Robot Teams"

Connected through hyperedge HE-6 (induced by source paper "Using Communication to Reduce Locality in Distributed Multi-Agent Learning"):
  with members:
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks"
    - "Reinforcement Learning in the Multi-Robot Domain"

Connected through hyperedge HE-7 (induced by source paper "Learning in Multi-Robot Systems"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"

Connected through hyperedge HE-8 (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"
    - "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"
    - "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