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: "Approximating Optimal Policies for Partially Observable Stochastic Domains"
  Abstract: The problem of making optimal decisions in uncertain conditions is central to Artificial Intelligence. If the state of the world is known at all times, the world can be modeled as a Markov Decision Process (MDP). MDPs have been studied extensively and many methods are known for determining optimal courses of action, or policies. The more realistic case where state information is only partially observable, Partially Observable Markov Decision Processes (POMDPs), have received much less attention. The best exact algorithms for these problems can be very inefficient in both space and time. We introduce Smooth Partially Observable Value Approximation (SPOVA), a new approximation method that can quickly yield good approximations which can improve over time. This method can be combined with reinforcement learning methods, a combination that was very effective in our test cases.

Connected through hyperedge HE-1 (induced by source paper "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation"):
  with members:
    - "Learning policies for partially observable environments: Scaling up"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Learning sorting and decision trees with POMDPs"

Connected through hyperedge HE-2 (induced by source paper "Rationality and Intelligence"):
  with members:
    - "Toward Efficient Agnostic Learning"
    - "The BATmobile: Towards a Bayesian Automated Taxi"
    - "A Formalization of Explanation-Based Macro-operator Learning"

Connected through hyperedge HE-3 (induced by source paper "Efficient dynamic-programming updates in partially observable Markov decision processes"):
  with members:
    - "Learning policies for partially observable environments: Scaling up"
    - "Incremental methods for computing bounds in partially observable Markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "Incremental methods for computing bounds in partially observable Markov decision processes"):
  with members:
    - "Learning policies for partially observable environments: Scaling up"
    - "Efficient dynamic-programming updates in partially observable Markov decision processes"

Connected through hyperedge HE-5 (induced by source paper "Adaptive probabilistic networks"):
  with members:
    - "The BATmobile: Towards a Bayesian Automated Taxi"

Connected through hyperedge HE-6 (induced by source paper "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"):
  with members:
    - "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in"
    - "High-Performance Job-Shop Scheduling With A Time-Delay TD() Network"
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "A Reinforcement Learning Approach to Job-shop Scheduling"

Connected through hyperedge HE-7 (induced by source paper "Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"
    - "Learning Without State-Estimation in Partially Observable Markovian Decision Processes"

Connected through hyperedge HE-8 (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"
    - "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation"
    - "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