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: "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions"
  Abstract: This paper presents exact solutions and convergent approximations for inferences in Bayesian networks associated with finitely generated convex sets of distributions. Robust Bayesian inference is the calculation of bounds on posterior values given perturbations in a probabilistic model. The paper presents exact inference algorithms and analyzes the circumstances where exact inference becomes intractable. Two classes of algorithms for numeric approximations are developed through transformations on the original model. The first transformation reduces the robust inference problem to the estimation of probabilistic parameters in a Bayesian network. The second transformation uses Lavine's bracketing algorithm to generate a sequence of maximization problems in a Bayesian network. The analysis is extended to the *-contaminated, the lower density bounded, the belief function, the sub-sigma, the density bounded, the total variation and the density ratio classes of distributions. c fl1996 Carnegie Mellon University

Connected through hyperedge HE-1 (induced by source paper "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Chain graphs for learning"
    - "Automated Decomposition of Model-based Learning Problems"
    - "Learning Limited Dependence Bayesian Classifiers"
    - "Update rules for parameter estimation in Bayesian networks"
    - "Discovering Structure in Continuous Variables Using Bayesian Networks"
    - "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"

Connected through hyperedge HE-2 (induced by source paper "Robustness Analysis of Bayesian Networks with Global Neighborhoods"):
  with members:
    - "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"
    - "Exploiting Causal Independence in Bayesian Network Inference"

Connected through hyperedge HE-3 (induced by source paper "Exploiting Causal Independence in Bayesian Network Inference"):
  with members:
    - "Context-Specific Independence in Bayesian Networks"
    - "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"
    - "Robustness Analysis of Bayesian Networks with Global Neighborhoods"
    - "Incremental Tradeoff Resolution in Qualitative Probabilistic Networks"

Connected through hyperedge HE-4 (induced by source paper "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"):
  with members:
    - "Context-Specific Independence in Bayesian Networks"
    - "Exploiting Causal Independence in Bayesian Network Inference"
    - "Robustness Analysis of Bayesian Networks with Global Neighborhoods"
    - "Topological Parameters for time-space tradeoff"
    - "Global Conditioning for Probabilistic Inference in Belief Networks"

Connected through hyperedge HE-5 (induced by source paper "Using Qualitative Relationships for Bounding Probability Distributions"):
  with members:
    - "Computing upper and lower bounds on likelihoods in intractable networks"
    - "Incremental Tradeoff Resolution in Qualitative Probabilistic Networks"
    - "State-Space Abstraction for Anytime Evaluation of Probabilistic Networks"
    - "Localized Partial Evaluation of Belief 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