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: "Q-Learning with Hidden-Unit Restarting"
  Abstract: Platt's resource-allocation network (RAN) (Platt, 1991a, 1991b) is modified for a reinforcement-learning paradigm and to "restart" existing hidden units rather than adding new units. After restarting, units continue to learn via back-propagation. The resulting restart algorithm is tested in a Q-learning network that learns to solve an inverted pendulum problem. Solutions are found faster on average with the restart algorithm than without it.

Connected through hyperedge HE-1 (induced by source paper "Reinforcement Learning with Modular Neural Networks for Control"):
  with members:
    - "Strategy Learning with Multilayer Connectionist Representations 1"

Connected through hyperedge HE-2 (induced by source paper "Learning to Act using Real-Time Dynamic Programming"):
  with members:
    - "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in"
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
    - "Learning sorting and decision trees with POMDPs"
    - "Markov Decision Processes in Large State Spaces"
    - "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards"
    - "The Efficient Learning of Multiple Task Sequences"
    - "Exploration and Model Building in Mobile Robot Domains"

Connected through hyperedge HE-3 (induced by source paper "Reinforcement Learning, Neural Networks and PI Control Applied to a Heating Coil"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"

Connected through hyperedge HE-4 (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"
    - "Reinforcement Learning with Modular Neural Networks for Control"
    - "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-5 (induced by source paper "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"):
  with members:
    - "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in"
    - "High-Performance Job-Shop Scheduling With A Time-Delay TD() Network"
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "A Reinforcement Learning Approach to Job-shop Scheduling"

Connected through hyperedge HE-6 (induced by source paper "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"
    - "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques"
    - "A Neural Network Pole Balancer that Learns and Operates on a Real Robot in Real Time"
    - "Strategy Learning with Multilayer Connectionist Representations 1"

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