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: "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.

Connected through hyperedge HE-1 (induced by source paper "Efficient Inference in Bayes Networks As A Combinatorial Optimization Problem"):
  with members:
    - "Some Experiments with Real-time Decision Algorithms"
    - "Case-Based Probability Factoring in Bayesian Belief Networks"
    - "Sensitivities: An Alternative to Conditional Probabilities for Bayesian Belief Networks"
    - "Interpretation of Complex Scenes Using Bayesian 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