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: "Learning to coordinate without sharing information"
  Abstract: Researchers in the field of Distributed Artificial Intelligence (DAI) have been developing efficient mechanisms to coordinate the activities of multiple autonomous agents. The need for coordination arises because agents have to share resources and expertise required to achieve their goals. Previous work in the area includes using sophisticated information exchange protocols, investigating heuristics for negotiation, and developing formal models of possibilities of conflict and cooperation among agent interests. In order to handle the changing requirements of continuous and dynamic environments, we propose learning as a means to provide additional possibilities for effective coordination. We use reinforcement learning techniques on a block pushing problem to show that agents can learn complimentary policies to follow a desired path without any knowledge about each other. We theoretically analyze and experimentally verify the effects of learning rate on system convergence, and demonstrate benefits of using learned coordination knowledge on similar problems. Reinforcement learning based coordination can be achieved in both cooperative and non-cooperative domains, and in domains with noisy communication channels and other stochastic characteristics that present a formidable challenge to using other coordination schemes.

Connected through hyperedge HE-1 (induced by source paper "Adaptive Load Balancing: A Study in Multi-Agent Learning"):
  with members:
    - "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"

Connected through hyperedge HE-2 (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,"
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "Reinforcement Learning: A Survey"

Connected through hyperedge HE-3 (induced by source paper "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Exploration and Model Building in Mobile Robot Domains"
    - "Strategy Learning with Multilayer Connectionist Representations 1"
    - "Least-Squares Temporal Difference Learning"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"

Connected through hyperedge HE-4 (induced by source paper "Figure 3: Average model size accepted from a ran-dom prefix-closed samples of various size, and"):
  with members:
    - "Markov games as a framework for multi-agent reinforcement learning"

Connected through hyperedge HE-5 (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"
    - "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