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 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

Includes members:
  Member "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Context-Specific Independence in Bayesian Networks")
  Member "Exploiting Causal Independence in Bayesian Network Inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Context-Specific Independence in Bayesian Networks")
  Member "Using Qualitative Relationships for Bounding Probability Distributions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Incremental Tradeoff Resolution in Qualitative Probabilistic Networks")
  Member "Robustness Analysis of Bayesian Networks with Global Neighborhoods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Exploiting Causal Independence in Bayesian Network Inference")
  Member "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Update rules for parameter estimation in Bayesian networks")

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