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 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.

Connected through hyperedge HE-1 (induced by source paper "Strategy Learning with Multilayer Connectionist Representations 1"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Q-Learning with Hidden-Unit Restarting"
    - "NEUROCONTROL BY REINFORCEMENT LEARNING"
    - "Learning to Race: Experiments with a Simulated Race Car"
    - "On the Computational Economics of Reinforcement Learning"
    - "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning"

Connected through hyperedge HE-2 (induced by source paper "Q-Learning with Hidden-Unit Restarting"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Strategy Learning with Multilayer Connectionist Representations 1"
    - "Reinforcement Learning, Neural Networks and PI Control Applied to a Heating Coil"

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