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: "TD Models: Modeling the World at a Mixture of Time Scales"
  Abstract: Temporal-difference (TD) learning can be used not just to predict rewards, as is commonly done in reinforcement learning, but also to predict states, i.e., to learn a model of the world's dynamics. We present theory and algorithms for intermixing TD models of the world at different levels of temporal abstraction within a single structure. Such multi-scale TD models can be used in model-based reinforcement-learning architectures and dynamic programming methods in place of conventional Markov models. This enables planning at higher and varied levels of abstraction, and, as such, may prove useful in formulating methods for hierarchical or multi-level planning and reinforcement learning. In this paper we treat only the prediction problem|that of learning a model and value function for the case of fixed agent behavior. Within this context, we establish the theoretical foundations of multi-scale models and derive TD algorithms for learning them. Two small computational experiments are presented to test and illustrate the theory. This work is an extension and generalization of the work of Singh (1992), Dayan (1993), and Sutton & Pinette (1985).

Connected through hyperedge HE-1 (induced by source paper "Planning and Learning in an Adversarial Robotic Game"):
  with members:
    - "Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries"
    - "Learning DFA from Simple Examples"

Connected through hyperedge HE-2 (induced by source paper "Multi-Time Models for Reinforcement Learning"):
  with members:
    - "Multi-time Models for Temporally Abstract Planning"

Connected through hyperedge HE-3 (induced by source paper "Multi-time Models for Temporally"):
  with members:
    - "Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models"
    - "Analytical Mean Squared Error Curves for Temporal Difference Learning"
    - "Multi-time Models for Temporally Abstract Planning"

Connected through hyperedge HE-4 (induced by source paper "Multi-time Models for Temporally Abstract Planning"):
  with members:
    - "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"
    - "Multi-Time Models for Reinforcement Learning"

Connected through hyperedge HE-5 (induced by source paper "Planning with Closed-Loop Macro Actions"):
  with members:
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "Hierarchical Recurrent Neural Networks for Long-Term Dependencies"):
  with members:
    - "GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS"
    - "Diffusion of Context and Credit Information in Markovian Models"
    - "Boltzmann Chains and Hidden Markov 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