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: "Q2: Memory-based active learning for optimizing noisy continuous functions field expands beyond prediction and function"
  Abstract: This paper introduces a new algorithm, Q2, for optimizing the expected output of a multi-input noisy continuous function. Q2 is designed to need only a few experiments, it avoids strong assumptions on the form of the function, and it is autonomous in that it requires little problem-specific tweaking. Four existing approaches to this problem (response surface methods, numerical optimization, supervised learning, and evolutionary methods) all have inadequacies when the requirement of "black box" behavior is combined with the need for few experiments. Q2 uses instance-based determination of a convex region of interest for performing experiments. In conventional instance-based approaches to learning, a neighborhood was defined by proximity to a query point. In contrast, Q2 defines the neighborhood by a new geometric procedure that captures the size and

Connected through hyperedge HE-1 (induced by source paper "Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators"):
  with members:
    - "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"
    - "Category: Control, Navigation and Planning Preference: Oral presentation Exploiting Model Uncertainty Estimates for Safe Dynamic"

Connected through hyperedge HE-2 (induced by source paper "In  Active Learning with Statistical Models"):
  with members:
    - "Supervised learning from incomplete data via an EM approach"
    - "In:  A Mixture Model System for Medical and Machine Diagnosis"
    - "Information-based objective functions for active data selection"
    - "Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning"
    - "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."
    - "Neural Network Exploration Using Optimal Experiment Design"

Connected through hyperedge HE-3 (induced by source paper "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning"):
  with members:
    - "In  Improving Elevator Performance Using Reinforcement Learning"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Value Function Based Production Scheduling"
    - "Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes"
    - "Reinforcement Learning Methods for Continuous-Time Markov Decision Problems"

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