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: "Planning with Closed-Loop Macro Actions"
  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. Conventional model-based reinforcement learning uses primitive actions that last one time step and that can be modeled independently of the learning agent. These can be generalized to macro actions, multi-step actions specified by an arbitrary policy and a way of completing. Macro actions generalize the classical notion of a macro operator in that they are closed loop, uncertain, and of variable duration. Macro actions are needed to represent common-sense higher-level actions such as going to lunch, grasping an object, or traveling to a distant city. This paper generalizes prior work on temporally abstract models (Sutton 1995) and extends it from the prediction setting to include actions, control, and planning. We define a semantics of models of macro actions that guarantees the validity of planning using such models. This paper present new results in the theory of planning with macro actions and illustrates its potential advantages in a gridworld task.

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

Connected through hyperedge HE-2 (induced by source paper "Roles of Macro-Actions in Accelerating Reinforcement Learning"):
  with members:
    - "A Heuristic Approach to the Discovery of Macro-operators. Machine Learning, 3, 285-317. L e a"
    - "Multi-time Models for Temporally Abstract Planning"

Connected through hyperedge HE-3 (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"
    - "Hierarchical Recurrent Neural Networks for Long-Term Dependencies"
    - "Multi-time Models for Temporally Abstract Planning"
    - "Multi-time Models for Temporally"
    - "Multi-Time Models for Reinforcement Learning"

Connected through hyperedge HE-4 (induced by source paper "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Exploration and Model Building in Mobile Robot Domains"
    - "Strategy Learning with Multilayer Connectionist Representations 1"
    - "Least-Squares Temporal Difference Learning"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"

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