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: "Sensitivities: An Alternative to Conditional Probabilities for Bayesian Belief Networks"
  Abstract: We show an alternative way of representing a Bayesian belief network by sensitivities and probability distributions. This representation is equivalent to the traditional representation by conditional probabilities, but makes dependencies between nodes apparent and intuitively easy to understand. We also propose a QR matrix representation for the sensitivities and/or conditional probabilities which is more efficient, in both memory requirements and computational speed, than the traditional representation for computer-based implementations of probabilistic inference. We use sensitivities to show that for a certain class of binary networks, the computation time for approximate probabilistic inference with any positive upper bound on the error of the result is independent of the size of the network. Finally, as an alternative to traditional algorithms that use conditional probabilities, we describe an exact algorithm for probabilistic inference that uses the QR-representation for sensitivities and updates probability distributions of nodes in a network according to messages from the neigh bors.

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"
    - "Algebraic Techniques for Efficient Inference in Bayesian Networks"
    - "Interpretation of Complex Scenes Using Bayesian Networks"

Connected through hyperedge HE-2 (induced by source paper "Computational complexity reduction for BN2O networks using similarity of states"):
  with members:
    - "State-Space Abstraction for Anytime Evaluation of Probabilistic 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