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: "Adaptive Load Balancing: A Study in Multi-Agent Learning"
  Abstract: We study the process of multi-agent reinforcement learning in the context of load balancing in a distributed system, without use of either central coordination or explicit communication. We first define a precise framework in which to study adaptive load balancing, important features of which are its stochastic nature and the purely local information available to individual agents. Given this framework, we show illuminating results on the interplay between basic adaptive behavior parameters and their effect on system efficiency. We then investigate the properties of adaptive load balancing in heterogeneous populations, and address the issue of exploration vs. exploitation in that context. Finally, we show that naive use of communication may not improve, and might even harm system efficiency.

Connected through hyperedge HE-1 (induced by source paper "Learning to coordinate without sharing information"):
  with members:
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"
    - "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems"
    - "Figure 3: Average model size accepted from a ran-dom prefix-closed samples of various size, and"

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"
    - "Using Communication to Reduce Locality in Distributed 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