human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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).

Includes members:
  Member "Planning and Learning in an Adversarial Robotic Game":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Learning DFA from Simple Examples")
  Member "Planning with Closed-Loop Macro Actions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models")
  Member "Hierarchical Recurrent Neural Networks for Long-Term Dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Diffusion of Context and Credit Information in Markovian Models")
  Member "Multi-time Models for Temporally Abstract Planning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Multi-Time Models for Reinforcement Learning")
  Member "Multi-time Models for Temporally":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models")
  Member "Multi-Time Models for Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Multi-time Models for Temporally Abstract Planning")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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