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: "Logarithmic-Time Updates and Queries in Probabilistic Networks"
  Abstract: Traditional databases commonly support efficient query and update procedures that operate in time which is sublinear in the size of the database. Our goal in this paper is to take a first step toward dynamic reasoning in probabilistic databases with comparable efficiency. We propose a dynamic data structure that supports efficient algorithms for updating and querying singly connected Bayesian networks. In the conventional algorithm, new evidence is absorbed in time O(1) and queries are processed in time O(N ), where N is the size of the network. We propose an algorithm which, after a preprocessing phase, allows us to answer queries in time O(log N ) at the expense of O(log N ) time per evidence absorption. The usefulness of sub-linear processing time manifests itself in applications requiring (near) real-time response over large probabilistic databases. We briefly discuss a potential application of dynamic probabilistic reasoning in computational biology.

Connected through hyperedge HE-1 (induced by source paper "Towards a Better Understanding of Memory-Based Reasoning Systems"):
  with members:
    - "Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "EXPLORING A FRAMEWORK FOR INSTANCE BASED LEARNING AND NAIVE BAYESIAN CLASSIFIERS"

Connected through hyperedge HE-2 (induced by source paper "Logarithmic Time Parallel Bayesian Inference"):
  with members:
    - "Global Conditioning for Probabilistic Inference in Belief 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