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: "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation"
  Abstract: Discrete Bayesian models have been used to model uncertainty for mobile-robot navigation, but the question of how actions should be chosen remains largely unexplored. This paper presents the optimal solution to the problem, formulated as a partially observable Markov decision process. Since solving for the optimal control policy is intractable, in general, it goes on to explore a variety of heuristic control strategies. The control strategies are compared experimentally, both in simulation and in runs on a robot.

Connected through hyperedge HE-1 (induced by source paper "Approximating Optimal Policies for Partially Observable Stochastic Domains"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Learning policies for partially observable environments: Scaling up"
    - "Incremental methods for computing bounds in partially observable Markov decision processes"
    - "Efficient dynamic-programming updates in partially observable Markov decision processes"
    - "Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems"
    - "Rationality and Intelligence"
    - "Adaptive probabilistic networks"

Connected through hyperedge HE-2 (induced by source paper "Learning sorting and decision trees with POMDPs"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Learning policies for partially observable environments: Scaling up"

Connected through hyperedge HE-3 (induced by source paper "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"):
  with members:
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "Markov Decision Processes in Large State Spaces"
    - "A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms"
    - "Asynchronous Modified Policy Iteration with Single-sided Updates"
    - "Auto-exploratory Average Reward Reinforcement Learning"
    - "On the Complexity of Solving Markov Decision Problems"
    - "Improving Policies without Measuring Merits"

Connected through hyperedge HE-4 (induced by source paper "Learning policies for partially observable environments: Scaling up"):
  with members:
    - "Some Experiments with Real-time Decision Algorithms"
    - "A Formal Framework for Speedup Learning from Problems and Solutions"
    - "Approximating Optimal Policies for Partially Observable Stochastic Domains"
    - "Learning sorting and decision trees with POMDPs"
    - "Incremental methods for computing bounds in partially observable Markov decision processes"
    - "Efficient dynamic-programming updates in partially observable Markov decision processes"

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