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 "Algebraic Techniques for Efficient Inference in Bayesian Networks"):
  Abstract: A number of exact algorithms have been developed to perform probabilistic inference in Bayesian belief networks in recent years. These algorithms use graph-theoretic techniques to analyze and exploit network topology. In this paper, we examine the problem of efficient probabilistic inference in a belief network as a combinatorial optimization problem, that of finding an optimal factoring given an algebraic expression over a set of probability distributions. We define a combinatorial optimization problem, the optimal factoring problem, and discuss application of this problem in belief networks. We show that optimal factoring provides insight into the key elements of efficient probabilistic inference, and present simple, easily implemented algorithms with excellent performance. We also show how use of an algebraic perspective permits significant extension to the belief net representation.

Includes members:
  Member "Efficient Inference in Bayes Networks As A Combinatorial Optimization Problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Sensitivities: An Alternative to Conditional Probabilities for Bayesian Belief 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