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: "Using Communication to Reduce Locality in Distributed Multi-Agent Learning"
  Abstract: This paper attempts to bridge the fields of machine learning, robotics, and distributed AI. It discusses the use of communication in reducing the undesirable effects of locality in fully distributed multi-agent systems with multiple agents/robots learning in parallel while interacting with each other. Two key problems, hidden state and credit assignment, are addressed by applying local undirected broadcast communication in a dual role: as sensing and as reinforcement. The methodology is demonstrated on two multi-robot learning experiments. The first describes learning a tightly-coupled coordination task with two robots, the second a loosely-coupled task with four robots learning social rules. Communication is used to 1) share sensory data to overcome hidden state and 2) share reinforcement to overcome the credit assignment problem between the agents and bridge the gap between local/individual and global/group payoff.

Connected through hyperedge HE-1 (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,"
    - "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"
    - "Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning"

Connected through hyperedge HE-2 (induced by source paper "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"):
  with members:
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "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"
    - "Adaptive Load Balancing: A Study in Multi-Agent Learning"
    - "Team-Partitioned, Opaque-Transition Reinforcement Learning"

Connected through hyperedge HE-3 (induced by source paper "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks"):
  with members:
    - "Reinforcement Learning: A Survey"
    - "Learning Probabilistic Automata with Variable Memory Length"
    - "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces"
    - "Category: Control, Navigation and Planning. Key words: Reinforcement learning, Exploration, Hidden state. Prefer oral presentation."

Connected through hyperedge HE-4 (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"
    - "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"

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