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: "Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models"
  Abstract: The close connection between reinforcement learning (RL) algorithms and dynamic programming algorithms has fueled research on RL within the machine learning community. Yet, despite increased theoretical understanding, RL algorithms remain applicable to simple tasks only. In this paper I use the abstract framework afforded by the connection to dynamic programming to discuss the scaling issues faced by RL researchers. I focus on learning agents that have to learn to solve multiple structured RL tasks in the same environment. I propose learning abstract environment models where the abstract actions represent "intentions" of achieving a particular state. Such models are variable temporal resolution models because in different parts of the state space the abstract actions span different number of time steps. The operational definitions of abstract actions can be learned incrementally using repeated experience at solving RL tasks. I prove that under certain conditions solutions to new RL tasks can be found by using simu lated experience with abstract actions alone.

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"
    - "Analytical Mean Squared Error Curves for Temporal Difference Learning"
    - "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"
    - "Analytical Mean Squared Error Curves for Temporal Difference Learning"
    - "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"
    - "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