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: "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"
  Abstract: We present an algorithm for arc reversal in Bayesian networks with tree-structured conditional probability tables, and consider some of its advantages, especially for the simulation of dynamic probabilistic networks. In particular, the method allows one to produce CPTs for nodes involved in the reversal that exploit regularities in the conditional distributions. We argue that this approach alleviates some of the overhead associated with arc reversal, plays an important role in evidence integration and can be used to restrict sampling of variables in DPNs. We also provide an algorithm that detects the dynamic irrelevance of state variables in forward simulation. This algorithm exploits the structured CPTs in a reversed network to determine, in a time-independent fashion, the conditions under which a variable does or does not need to be sampled.

Connected through hyperedge HE-1 (induced by source paper "Context-Specific Independence in Bayesian Networks"):
  with members:
    - "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"
    - "Exploiting Causal Independence in Bayesian Network Inference"
    - "Structured Representation of Complex Stochastic Systems"
    - "The Frame Problem and Bayesian Network Action Representations"
    - "A Constraint-Based Approach to Preference Elicitation and Decision Making"
    - "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"

Connected through hyperedge HE-2 (induced by source paper "The Frame Problem and Bayesian Network Action Representations"):
  with members:
    - "Context-Specific Independence in Bayesian Networks"
    - "Structured Reachability Analysis for Markov Decision Processes"

Connected through hyperedge HE-3 (induced by source paper "Dynamic Belief Networks for Discrete Monitoring"):
  with members:
    - "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
    - "State-Space Abstraction for Anytime Evaluation of Probabilistic Networks"
    - "Introduction to the Special Section on Knowledge-Based Construction of Probabilistic and Decision Models (IEEE Transactions"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"
    - "Fall Diagnosis using Dynamic Belief Networks"

Connected through hyperedge HE-4 (induced by source paper "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"):
  with members:
    - "Context-Specific Independence in Bayesian Networks"
    - "Sequential Update of Bayesian Network Structure"
    - "A Tutorial on Learning With Bayesian Networks"
    - "On the Sample Complexity of Learning Bayesian Networks"

Connected through hyperedge HE-5 (induced by source paper "Stochastic simulation algorithms for dynamic probabilistic networks"):
  with members:
    - "Structured Representation of Complex Stochastic Systems"
    - "Dynamic Belief Networks for Discrete Monitoring"
    - "Tractable Inference for Complex Stochastic Processes"
    - "The BATmobile: Towards a Bayesian Automated Taxi"

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: Probabilistic_Methods