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: "The Frame Problem and Bayesian Network Action Representations"
  Abstract: We examine a number of techniques for representing actions with stochastic effects using Bayesian networks and influence diagrams. We compare these techniques according to ease of specification and size of the representation required for the complete specification of the dynamics of a particular system, paying particular attention the role of persistence relationships. We precisely characterize two components of the frame problem for Bayes nets and stochastic actions, propose several ways to deal with these problems, and compare our solutions with Re-iter's solution to the frame problem for the situation calculus. The result is a set of techniques that permit both ease of specification and compact representation of probabilistic system dynamics that is of comparable size (and timbre) to Reiter's representation (i.e., with no explicit frame axioms).

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 Arc Reversal and Simulation of Dynamic Probabilistic Networks"
    - "Structured Representation of Complex Stochastic Systems"
    - "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 "Structured Reachability Analysis for Markov Decision Processes"):
  with members:
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Correlated Action Effects in Decision Theoretic Regression"
    - "Approximating Value Trees in Structured Dynamic Programming"

Connected through hyperedge HE-3 (induced by source paper "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"):
  with members:
    - "Context-Specific Independence in Bayesian Networks"
    - "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"
    - "Dynamic Belief Networks for Discrete Monitoring"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"

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