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: "Quick 'n' Dirty Generalization For Mobile Robot Learning Content Areas: robotics, reinforcement learning, machine learning,"
  Abstract: The mobile robot domain challenges policy-iteration reinforcement learning algorithms with difficult problems of structural credit assignment and uncertainty. Structural credit assignment is particularly acute in domains where "real-time" trial length is a limiting factor on the number of learning steps that physical hardware can perform. Noisy sensors and effectors in complex dynamic environments further complicate the learning problem, leading to situations where speed of learning and policy flexibility may be more important than policy optimality. Input generalization addresses these problems but is typically too time consuming for robot domains. We present two algorithms, YB-learning and YB , that perform simple and fast generalization of the input space based on bit-similarity. The algorithms trade off long-term optimality for immediate performance and flexibility. The algorithms were tested in simulation against non-generalized learning across different numbers of discounting steps, and YB was shown to perform better during the earlier stages of learning, particularly in the presence of noise. In trials performed on a sonar-based mobile robot subject to uncertainty of the "real world," YB surpassed the simulation results by a wide margin, strongly supporting the role of such "quick and dirty" generalization strategies in noisy real-time mobile robot domains.

Connected through hyperedge HE-1 (induced by source paper "Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Learning To Play the Game of Chess"

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