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 with Modular Neural Networks for Control"):
  Abstract: Reinforcement learning methods can be applied to control problems with the objective of optimizing the value of a function over time. They have been used to train single neural networks that learn solutions to whole tasks. Jacobs and Jordan [5] have shown that a set of expert networks combined via a gating network can more quickly learn tasks that can be decomposed. Even the decomposition can be learned. Inspired by Boyan's work of modular neural networks for learning with temporal-difference methods [4], we modify the reinforcement learning algorithm called Q-Learning to train a modular neural network to solve a control problem. The resulting algorithm is demonstrated on the classical pole-balancing problem. The advantage of such a method is that it makes it possible to deal with complex dynamic control problem effectively by using task decomposition and competitive learning.

Includes members:
  Member "Q-Learning with Hidden-Unit Restarting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Strategy Learning with Multilayer Connectionist Representations 1":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming")

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