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: "Analytical Mean Squared Error Curves for Temporal Difference Learning"
  Abstract: We provide analytical expressions governing changes to the bias and variance of the lookup table estimators provided by various Monte Carlo and temporal difference value estimation algorithms with o*ine updates over trials in absorbing Markov reward processes. We have used these expressions to develop software that serves as an analysis tool: given a complete description of a Markov reward process, it rapidly yields an exact mean-square-error curve, the curve one would get from averaging together sample mean-square-error curves from an infinite number of learning trials on the given problem. We use our analysis tool to illustrate classes of mean-square-error curve behavior in a variety of example reward processes, and we show that although the various temporal difference algorithms are quite sensitive to the choice of step-size and eligibility-trace parameters, there are values of these parameters that make them similarly competent, and generally good.

Connected through hyperedge HE-1 (induced by source paper "Multi-time Models for Temporally"):
  with members:
    - "TD Models: Modeling the World at a Mixture of Time Scales"
    - "Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models"
    - "Multi-time Models for Temporally Abstract Planning"

Connected through hyperedge HE-2 (induced by source paper "Multi-time Models for Temporally Abstract Planning"):
  with members:
    - "TD Models: Modeling the World at a Mixture of Time Scales"
    - "Roles of Macro-Actions in Accelerating Reinforcement Learning"
    - "Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models"
    - "Multi-time Models for Temporally"
    - "Multi-Time Models for Reinforcement Learning"

Connected through hyperedge HE-3 (induced by source paper "Planning with Closed-Loop Macro Actions"):
  with members:
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "TD Models: Modeling the World at a Mixture of Time Scales"
    - "Roles of Macro-Actions in Accelerating Reinforcement Learning"
    - "Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models"

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