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: "Roles of Macro-Actions in Accelerating Reinforcement Learning"
  Abstract: We analyze the use of built-in policies, or macro-actions, as a form of domain knowledge that can improve the speed and scaling of reinforcement learning algorithms. Such macro-actions are often used in robotics, and macro-operators are also well-known as an aid to state-space search in AI systems. The macro-actions we consider are closed-loop policies with termination conditions. The macro-actions can be chosen at the same level as primitive actions. Macro-actions commit the learning agent to act in a particular, purposeful way for a sustained period of time. Overall, macro-actions may either accelerate or retard learning, depending on the appropriateness of the macro-actions to the particular task. We analyze their effect in a simple example, breaking the acceleration effect into two parts: 1) the effect of the macro-action in changing exploratory behavior, independent of learning, and 2) the effect of the macro-action on learning, independent of its effect on behavior. In our example, both effects are significant, but the latter appears to be larger. Finally, we provide a more complex gridworld illustration of how appropriately chosen macro-actions can accelerate overall learning.

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

Connected through hyperedge HE-2 (induced by source paper "A Heuristic Approach to the Discovery of Macro-operators. Machine Learning, 3, 285-317. L e a"):
  with members:
    - "Computational Learning in Humans and Machines"
    - "Utilization Filtering a method for reducing the inherent harmfulness of deductively learned knowledge field of"
    - "The Role of Forgetting in 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"
    - "Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models"
    - "Analytical Mean Squared Error Curves for Temporal Difference 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