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: "Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding"
  Abstract: On large problems, reinforcement learning systems must use parameterized function approximators such as neural networks in order to generalize between similar situations and actions. In these cases there are no strong theoretical results on the accuracy of convergence, and computational results have been mixed. In particular, Boyan and Moore reported at last year's meeting a series of negative results in attempting to apply dynamic programming together with function approximation to simple control problems with continuous state spaces. In this paper, we present positive results for all the control tasks they attempted, and for one that is significantly larger. The most important differences are that we used sparse-coarse-coded function approximators (CMACs) whereas they used mostly global function approximators, and that we learned online whereas they learned o*ine. Boyan and Moore and others have suggested that the problems they encountered could be solved by using actual outcomes ("rollouts"), as in classical Monte Carlo methods, and as in the TD() algorithm when = 1. However, in our experiments this always resulted in substantially poorer performance. We conclude that reinforcement learning can work robustly in conjunction with function approximators, and that there is little justification at present for avoiding the case of general .

Connected through hyperedge HE-1 (induced by source paper "Decision Tree Function Approximation in Reinforcement Learning"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"

Connected through hyperedge HE-2 (induced by source paper "Fast Online Q()"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Truncating Temporal Differences: On the Efficient Implementation of TD() for Reinforcement Learning"

Connected through hyperedge HE-3 (induced by source paper "Generality versus Size in Genetic Programming"):
  with members:
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Balancing Accuracy and Parsimony in Genetic Programming 1"
    - "Evolving Visual Routines"
    - "Evolving a Generalised Behaviour: Artificial Ant Problem Revisited"

Connected through hyperedge HE-4 (induced by source paper "Learning in Continuous Domains with Delayed Rewards"):
  with members:
    - "Emergent Hierarchical Control Structures: Learning Reactive/Hierarchical Relationships in Reinforcement Environments"
    - "Learning Hierarchical Control Structures for Multiple Tasks and Changing Environments"

Connected through hyperedge HE-5 (induced by source paper "Modeling the Student with Reinforcement Learning"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"

Connected through hyperedge HE-6 (induced by source paper "Applying Online Search Techniques to Continuous-State Reinforcement Learning key to the success of the local"):
  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"
    - "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces"

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