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 "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.

Includes members:
  Member "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "TDLeaf(): Combining Temporal Difference learning with game-tree search.")
  Member "Learning policies for partially observable environments: Scaling up":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation")
  Member "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning sorting and decision trees with POMDPs")
  Member "Incremental methods for computing bounds in partially observable Markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Efficient dynamic-programming updates in partially observable Markov decision processes")
  Member "Efficient dynamic-programming updates in partially observable Markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Incremental methods for computing bounds in partially observable Markov decision processes")
  Member "Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms")
  Member "Rationality and Intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "A Formalization of Explanation-Based Macro-operator Learning")
  Member "Adaptive probabilistic networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "The BATmobile: Towards a Bayesian Automated Taxi")

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