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: "Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators"
  Abstract: This paper focuses on the optimization of hyper-parameters for function approximators. We describe a kind of racing algorithm for continuous optimization problems that spends less time evaluating poor parameter settings and more time honing its estimates in the most promising regions of the parameter space. The algorithm is able to automatically optimize the parameters of a function approximator with less computation time. We demonstrate the algorithm on the problem of finding good parameters for a memory based learner and show the tradeoffs involved in choosing the right amount of computation to spend on each evaluation.

Connected through hyperedge HE-1 (induced by source paper "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "Using Errors to Create Piecewise Learnable Partitions"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "Selecting Input Variables Using Mutual Information and Nonparametric Density Estimation"

Connected through hyperedge HE-2 (induced by source paper "Category: Control, Navigation and Planning Preference: Oral presentation Exploiting Model Uncertainty Estimates for Safe Dynamic"):
  with members:
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Efficient Locally Weighted Polynomial Regression Predictions"
    - "Using Local Trajectory Optimizers To Speed Up Global Optimization In Dynamic Programming"

Connected through hyperedge HE-3 (induced by source paper "Q2: Memory-based active learning for optimizing noisy continuous functions field expands beyond prediction and function"):
  with members:
    - "In  Active Learning with Statistical Models"
    - "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning"

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