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: "Multi-Time Models for Reinforcement Learning"
  Abstract: Reinforcement learning can be used not only to predict rewards, but also to predict states, i.e. to learn a model of the world's dynamics. Models can be defined at different levels of temporal abstraction. Multi-time models are models that focus on predicting what will happen, rather than when a certain event will take place. Based on multi-time models, we can define abstract actions, which enable planning (presumably in a more efficient way) at various levels of abstraction.

Connected through hyperedge HE-1 (induced by source paper "TD Models: Modeling the World at a Mixture of Time Scales"):
  with members:
    - "Planning and Learning in an Adversarial Robotic Game"
    - "Planning with Closed-Loop Macro Actions"
    - "Hierarchical Recurrent Neural Networks for Long-Term Dependencies"
    - "Multi-time Models for Temporally Abstract Planning"
    - "Multi-time Models for Temporally"

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"
    - "Analytical Mean Squared Error Curves for Temporal Difference Learning"
    - "Multi-time Models for Temporally"

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