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: "Learning to Race: Experiments with a Simulated Race Car"
  Abstract: We have implemented a reinforcement learning architecture as the reactive component of a two layer control system for a simulated race car. We have found that separating the layers has expedited gradually improving competition and mult-agent interaction. We ran experiments to test the tuning, decomposition and coordination of the low level behaviors. We then extended our control system to allow passing of other cars and tested its ability to avoid collisions. The best design used reinforcement learning with separate networks for each behavior, coarse coded input and a simple rule based coordination mechanism.

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"
    - "Reinforcement Learning with Modular Neural Networks for Control"
    - "NEUROCONTROL BY REINFORCEMENT LEARNING"
    - "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 "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-3 (induced by source paper "Robot Shaping: Developing Situated Agents through Learning"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Adapting Control Strategies for Situated Autonomous Agents"
    - "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"
    - "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning"
    - "REINFORCEMENT LEARNING FOR COORDINATED REACTIVE CONTROL"
    - "The Role of the Trainer in Reinforcement Learning"
    - "An unsupervised neural network for low-level control of a wheeled mobile robot: noise resistance, stability,"

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