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 Temporally"
  Abstract: Planning Abstract Planning and learning at multiple levels of temporal abstraction is a key problem for artificial intelligence. In this paper we summarize an approach to this problem based on the mathematical framework of Markov decision processes and reinforcement learning. Current model-based reinforcement learning is based on one-step models that cannot represent common-sense higher-level actions, such as going to lunch, grasping an object, or flying to Denver. This paper generalizes prior work on temporally abstract models (Sutton, 1995b) and extends it from the prediction setting to include actions, control, and planning. We introduce a more general form of temporally abstract model, the multi-time model, and establish its suitability for planning and learning by virtue of its relationship to Bellman equations. This paper summarizes the theoretical framework of multi-time models and illustrates their potential ad The need for hierarchical and abstract planning is a fundamental problem in AI (see, e.g., Sacerdoti, 1977; Laird et al., 1986; Korf, 1985; Kaelbling, 1993; Dayan & Hinton, 1993). Model-based reinforcement learning offers a possible solution to the problem of integrating planning with real-time learning and decision-making (Peng & Williams, 1993, Moore & Atkeson, 1993; Sutton and Barto, in press). However, current model-based reinforcement learning is based on one-step models that cannot represent common-sense, higher-level actions. Modeling such actions requires the ability to handle different, interrelated levels of temporal abstraction. A new approach to modeling at multiple time scales was introduced by Sutton (1995b) based on prior work by Singh (1992), Dayan (1993b), and Sutton and Pinette (1985). This approach enables models of the environment at different temporal scales to be intermixed, producing temporally abstract models. However, that work was concerned only with predicting the environment. This paper summarizes vantages in a gridworld planning task.

Connected through hyperedge HE-1 (induced by source paper "Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models"):
  with members:
    - "Planning with Closed-Loop Macro Actions"
    - "Multi-time Models for Temporally Abstract Planning"

Connected through hyperedge HE-2 (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 Reinforcement Learning"

Connected through hyperedge HE-3 (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 Reinforcement 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