human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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

Includes members:
  Member "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions")
  Member "Exploiting Causal Independence in Bayesian Network Inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions")
  Member "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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