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 FOR COORDINATED REACTIVE CONTROL"
  Abstract: The demands of rapid response and the complexity of many environments make it difficult to decompose, tune and coordinate reactive behaviors while ensuring consistency. Reinforcement learning networks can address the tuning problem, but do not address the problem of decomposition and coordination. We hypothesize that interacting reactions can often be decomposed into separate control tasks resident in separate networks and that the interaction can be coordinated through the tuning mechanism and a higher level controller. To explore these issues, we have implemented a reinforcement learning architecture as the reactive component of a two layer control system for a simulated race car. By varying the architecture, we test whether decomposing reactivity into separate controllers leads to superior overall performance and learning convergence in our domain.

Connected through hyperedge HE-1 (induced by source paper "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"):
  with members:
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"
    - "Symbolic and Subsymbolic Learning for Vision: Some Possibilities"
    - "Forward models: Supervised learning with a distal teacher"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning"
    - "Refining PID Controllers using Neural Networks"

Connected through hyperedge HE-2 (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"
    - "Learning to Race: Experiments with a Simulated Race Car"
    - "On the Computational Economics of Reinforcement Learning"

Connected through hyperedge HE-3 (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-4 (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"
    - "Learning to Race: Experiments with a Simulated Race Car"
    - "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"
    - "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: Neural_Networks