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: "Reinforcement Learning: A Survey"
  Abstract: This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word "reinforcement." The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.

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,"
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "Learning to coordinate without sharing information"

Connected through hyperedge HE-2 (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"
    - "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"
    - "Learning Roles: Behavioral Diversity in Robot Teams"

Connected through hyperedge HE-3 (induced by source paper "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks"):
  with members:
    - "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."
    - "Using Communication to Reduce Locality in Distributed Multi-Agent Learning"

Connected through hyperedge HE-4 (induced by source paper "Adding Memory to XCS"):
  with members:
    - "A Study of the Generalization Capabilities of XCS"

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