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: "Decision Tree Function Approximation in Reinforcement Learning"
  Abstract: We present a decision tree based approach to function approximation in reinforcement learning. We compare our approach with table lookup and a neural network function approximator on three problems: the well known mountain car and pole balance problems as well as a simulated automobile race car. We find that the decision tree can provide better learning performance than the neural network function approximation and can solve large problems that are infeasible using table lookup.

Connected through hyperedge HE-1 (induced by source paper "Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding"):
  with members:
    - "Applying Online Search Techniques to Continuous-State Reinforcement Learning key to the success of the local"
    - "Fast Online Q()"
    - "Modeling the Student with Reinforcement Learning"
    - "Learning in Continuous Domains with Delayed Rewards"
    - "Generality versus Size in Genetic Programming"

Connected through hyperedge HE-2 (induced by source paper "Generalization in Reinforcement Learning: Safely Approximating the Value Function"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
    - "A Reinforcement Learning Approach to Job-shop Scheduling"
    - "Value Function Approximations and Job-Shop Scheduling"
    - "Robust Value Function Approximation by Working Backwards Computing an accurate value function is the key"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"
    - "Issues in Using Function Approximation for Reinforcement Learning"
    - "Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions"

Connected through hyperedge HE-3 (induced by source paper "A System for Induction of Oblique Decision Trees"):
  with members:
    - "A hierarchical ensemble of decision trees applied to classifying data from a psychological experiment"
    - "Lazy Induction Triggered by CBR"
    - "Induction of Oblique Decision Trees"
    - "Geometry in Learning"
    - "Lookahead and Pathology in Decision Tree Induction"
    - "A Decision Tree System for Finding Genes in DNA"
    - "Decision Tree Induction: How Effective is the Greedy Heuristic?"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"

Connected through hyperedge HE-4 (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,"
    - "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"
    - "Q-Learning with Hidden-Unit Restarting"
    - "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