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 "Global Conditioning for Probabilistic Inference in Belief Networks"):
  Abstract: In this paper we propose a new approach to probabilistic inference on belief networks, global conditioning, which is a simple generalization of Pearl's (1986b) method of loop-cutset conditioning. We show that global conditioning, as well as loop-cutset conditioning, can be thought of as a special case of the method of Lauritzen and Spiegelhalter (1988) as refined by Jensen et al (1990a; 1990b). Nonetheless, this approach provides new opportunities for parallel processing and, in the case of sequential processing, a tradeoff of time for memory. We also show how a hybrid method (Suermondt and others 1990) combining loop-cutset conditioning with Jensen's method can be viewed within our framework. By exploring the relationships between these methods, we develop a unifying framework in which the advantages of each approach can be combined successfully.

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 "Structured Representation of Complex Stochastic Systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Context-Specific Independence in Bayesian Networks")
  Member "Topological Parameters for time-space tradeoff":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE")
  Member "Logarithmic Time Parallel Bayesian Inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Logarithmic-Time Updates and Queries in Probabilistic Networks")
  Member "Automated Decomposition of Model-based Learning Problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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