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: "Exploiting Causal Independence in Bayesian Network Inference"
  Abstract: A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A Bayesian network can be viewed as representing a factorization of a joint probability into the multiplication of a set of conditional probabilities. We present a notion of causal independence that enables one to further factorize the conditional probabilities into a combination of even smaller factors and consequently obtain a finer-grain factorization of the joint probability. The new formulation of causal independence lets us specify the conditional probability of a variable given its parents in terms of an associative and commutative operator, such as or, sum or max, on the contribution of each parent. We start with a simple algorithm VE for Bayesian network inference that, given evidence and a query variable, uses the factorization to find the posterior distribution of the query. We show how this algorithm can be extended to exploit causal independence. Empirical studies, based on the CPCS networks for medical diagnosis, show that this method is more efficient than previous methods and allows for inference in larger networks than previous algorithms.

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"
    - "Using Qualitative Relationships for Bounding Probability Distributions"
    - "Robustness Analysis of Bayesian Networks with Global Neighborhoods"
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"

Connected through hyperedge HE-2 (induced by source paper "Context-Specific Independence in Bayesian Networks"):
  with members:
    - "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"
    - "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"
    - "Structured Representation of Complex Stochastic Systems"
    - "The Frame Problem and Bayesian Network Action Representations"
    - "A Constraint-Based Approach to Preference Elicitation and Decision Making"
    - "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"

Connected through hyperedge HE-3 (induced by source paper "Robustness Analysis of Bayesian Networks with Global Neighborhoods"):
  with members:
    - "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"
    - "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions"

Connected through hyperedge HE-4 (induced by source paper "Incremental Tradeoff Resolution in Qualitative Probabilistic Networks"):
  with members:
    - "Using Qualitative Relationships for Bounding Probability Distributions"
    - "State-Space Abstraction for Anytime Evaluation of Probabilistic Networks"
    - "Some Varieties of Qualitative Probability"

Connected through hyperedge HE-5 (induced by source paper "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"):
  with members:
    - "Context-Specific Independence in Bayesian Networks"
    - "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"
    - "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