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 "Generalized Prioritized Sweeping"):
  Abstract: Prioritized sweeping is a model-based reinforcement learning method that attempts to focus an agent's limited computational resources to achieve a good estimate of the value of environment states. To choose effectively where to spend a costly planning step, classic prioritized sweeping uses a simple heuristic to focus computation on the states that are likely to have the largest errors. In this paper, we introduce generalized prioritized sweeping, a principled method for generating such estimates in a representation-specific manner. This allows us to extend prioritized sweeping beyond an explicit, state-based representation to deal with compact representations that are necessary for dealing with large state spaces. We apply this method for generalized model approximators (such as Bayesian networks), and describe preliminary experiments that compare our approach with classical prioritized sweeping.

Includes members:
  Member "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION")
  Member "Sequential Update of Bayesian Network Structure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning Belief Networks in the Presence of Missing Values and Hidden Variables")
  Member "Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "On the Complexity of Solving Markov Decision Problems")
  Member "A Tutorial on Learning With Bayesian Networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Update rules for parameter estimation in Bayesian networks")

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