human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms")
  Member "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces")
  Member "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning to coordinate without sharing information")
  Member "Adding Memory to XCS":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A Study of the Generalization Capabilities of XCS")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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