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: "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.

Connected through hyperedge HE-1 (induced by source paper "Automated Decomposition of Model-based Learning Problems"):
  with members:
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"
    - "A Tutorial on Learning With Bayesian Networks"
    - "on Qualitative Reasoning about Physical Systems  Deriving Monotonic Function Envelopes from Observations"
    - "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration"
    - "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration"
    - "A Divide-and-Conquer Approach to Learning from Prior Knowledge"

Connected through hyperedge HE-2 (induced by source paper "Topological Parameters for time-space tradeoff"):
  with members:
    - "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"

Connected through hyperedge HE-3 (induced by source paper "Logarithmic Time Parallel Bayesian Inference"):
  with members:
    - "Logarithmic-Time Updates and Queries in 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"
    - "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions"
    - "Topological Parameters for time-space tradeoff"

Connected through hyperedge HE-5 (induced by source paper "Structured Representation of Complex Stochastic Systems"):
  with members:
    - "Context-Specific Independence in Bayesian Networks"
    - "Factorial Hidden Markov Models"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"
    - "Tractable Inference for Complex Stochastic Processes"

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