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: "Locally Weighted Learning for Control"
  Abstract: Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We explain various forms that control tasks can take, and how this affects the choice of learning paradigm. The discussion section explores the interesting impact that explicitly remembering all previous experiences has on the problem of learning to control.

Connected through hyperedge HE-1 (induced by source paper "Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "In  Active Learning with Statistical Models"
    - "Forward models: Supervised learning with a distal teacher"
    - "Efficient Locally Weighted Polynomial Regression Predictions"
    - "Reinforcement Learning in the Multi-Robot Domain"
    - "Using Local Trajectory Optimizers To Speed Up Global Optimization In Dynamic Programming"

Connected through hyperedge HE-2 (induced by source paper "Issues in Using Function Approximation for Reinforcement Learning"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"
    - "Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions"
    - "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"
    - "Learning To Play the Game of Chess"

Connected through hyperedge HE-3 (induced by source paper "Online Learning with Random Representations"):
  with members:
    - "An Improved Algorithm for Incremental Induction of Decision Trees"

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