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: "Efficient Inference in Bayes Networks As A Combinatorial Optimization Problem"
  Abstract: A number of exact algorithms have been developed to perform probabilistic inference in Bayesian belief networks in recent years. The techniques used in these algorithms are closely related to network structures and some of them are not easy to understand and implement. In this paper, we consider the problem from the combinatorial optimization point of view and state that efficient probabilistic inference in a belief network is a problem of finding an optimal factoring given a set of probability distributions. From this viewpoint, previously developed algorithms can be seen as alternate factoring strategies. In this paper, 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 demonstrate simple, easily implemented algorithms with excellent performance.

Connected through hyperedge HE-1 (induced by source paper "Sensitivities: An Alternative to Conditional Probabilities for Bayesian Belief Networks"):
  with members:
    - "Computational complexity reduction for BN2O networks using similarity of states"

Connected through hyperedge HE-2 (induced by source paper "Some Experiments with Real-time Decision Algorithms"):
  with members:
    - "Learning policies for partially observable environments: Scaling up"

Connected through hyperedge HE-3 (induced by source paper "Case-Based Probability Factoring in Bayesian Belief Networks"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "LIBGA: A USER-FRIENDLY WORKBENCH FOR ORDER-BASED GENETIC ALGORITHM RESEARCH"
    - "Decision-Theoretic Case-Based Reasoning"

Connected through hyperedge HE-4 (induced by source paper "Algebraic Techniques for Efficient Inference in Bayesian Networks"):
  with members:
    (none)

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