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 Global Neighborhoods"
  Abstract: This paper presents algorithms for robustness analysis of Bayesian networks with global neighborhoods. Robust Bayesian inference is the calculation of bounds on posterior values given perturbations in a probabilistic model. We present algorithms for robust inference (including expected utility, expected value and variance bounds) with global perturbations that can be modeled by *-contaminated, constant density ratio, constant density bounded and total variation classes of distributions. c fl1996 Carnegie Mellon University

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

Connected through hyperedge HE-2 (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 Finitely Generated Convex Sets of Distributions"
    - "Incremental Tradeoff Resolution in Qualitative Probabilistic Networks"

Connected through hyperedge HE-3 (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 Finitely Generated Convex Sets of Distributions"
    - "Topological Parameters for time-space tradeoff"
    - "Global Conditioning for Probabilistic Inference in 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