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: "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"
  Abstract: Probabilistic inference algorithms for belief updating, finding the most probable explanation, the maximum a posteriori hypothesis, and the maximum expected utility are reformulated within the bucket elimination framework. This emphasizes the principles common to many of the algorithms appearing in the probabilistic inference literature and clarifies the relationship of such algorithms to nonserial dynamic programming algorithms. A general method for combining conditioning and bucket elimination is also presented. For all the algorithms, bounds on complexity are given as a function of the problem's structure.

Connected through hyperedge HE-1 (induced by source paper "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions"):
  with members:
    - "Exploiting Causal Independence in Bayesian Network Inference"
    - "Using Qualitative Relationships for Bounding Probability Distributions"
    - "Robustness Analysis of Bayesian Networks with Global Neighborhoods"
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"

Connected through hyperedge HE-2 (induced by source paper "Context-Specific Independence in Bayesian Networks"):
  with members:
    - "Exploiting Causal Independence in Bayesian Network Inference"
    - "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"
    - "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-3 (induced by source paper "Robustness Analysis of Bayesian Networks with Global Neighborhoods"):
  with members:
    - "Exploiting Causal Independence in Bayesian Network Inference"
    - "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions"

Connected through hyperedge HE-4 (induced by source paper "Topological Parameters for time-space tradeoff"):
  with members:
    - "Global Conditioning for Probabilistic Inference in Belief Networks"

Connected through hyperedge HE-5 (induced by source paper "Exploiting Causal Independence in Bayesian Network Inference"):
  with members:
    - "Context-Specific Independence in Bayesian Networks"
    - "Robustness Analysis of Bayesian Networks with Global Neighborhoods"
    - "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions"
    - "Incremental Tradeoff Resolution in Qualitative Probabilistic Networks"

Connected through hyperedge HE-6 (induced by source paper "Global Conditioning for Probabilistic Inference in Belief Networks"):
  with members:
    - "Structured Representation of Complex Stochastic Systems"
    - "Topological Parameters for time-space tradeoff"
    - "Logarithmic Time Parallel Bayesian Inference"
    - "Automated Decomposition of Model-based Learning Problems"

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