human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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 .

Includes members:
  Member "Decision Tree Function Approximation in Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Generalization in Reinforcement Learning: Safely Approximating the Value Function")
  Member "Applying Online Search Techniques to Continuous-State Reinforcement Learning key to the success of the local":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces")
  Member "Fast Online Q()":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex.")
  Member "Modeling the Student with Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,")
  Member "Learning in Continuous Domains with Delayed Rewards":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Learning Hierarchical Control Structures for Multiple Tasks and Changing Environments")
  Member "Generality versus Size in Genetic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Evolving a Generalised Behaviour: Artificial Ant Problem Revisited")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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