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: "Interpretation of Complex Scenes Using Bayesian Networks"
  Abstract: In most object recognition systems, interactions between objects in a scene are ignored and the best interpretation is considered to be the set of hypothesized objects that matches the greatest number of image features. We show how image interpretation can be cast as the problem of finding the most probable explanation (MPE) in a Bayesian network that models both visual and physical object interactions. The problem of how to determine exact conditional probabilities for the network is shown to be unimportant, since the goal is to find the most probable configuration of objects, not to calculate absolute probabilities. We furthermore show that evaluating configurations by feature counting is equivalent to calculating the joint probability of the configuration using a restricted Bayesian network, and derive the assumptions about probabilities necessary to make a Bayesian formulation reasonable.

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"
    - "Algebraic Techniques for Efficient Inference in 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