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: "On the Sample Complexity of Learning Bayesian Networks"
  Abstract: In recent years there has been an increasing interest in learning Bayesian networks from data. One of the most effective methods for learning such networks is based on the minimum description length (MDL) principle. Previous work has shown that this learning procedure is asymptotically successful: with probability one, it will converge to the target distribution, given a sufficient number of samples. However, the rate of this convergence has been hitherto unknown. In this work we examine the sample complexity of MDL based learning procedures for Bayesian networks. We show that the number of samples needed to learn an *-close approximation (in terms of entropy distance) with confidence ffi is O * ) 3 log 1 ffi log log 1 . This means that the sample complexity is a low-order polynomial in the error threshold and sub-linear in the confidence bound. We also discuss how the constants in this term depend on the complexity of the target distribution. Finally, we address questions of asymptotic minimality and propose a method for using the sample complexity results to speed up the learning process.

Connected through hyperedge HE-1 (induced by source paper "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"):
  with members:
    - "Context-Specific Independence in Bayesian Networks"
    - "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"
    - "Sequential Update of Bayesian Network Structure"
    - "A Tutorial on Learning With Bayesian Networks"

Connected through hyperedge HE-2 (induced by source paper "A Tutorial on Learning With Bayesian Networks"):
  with members:
    - "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"
    - "Learning Bayesian Networks from Incomplete Data"
    - "Sequential Update of Bayesian Network Structure"
    - "Automated Decomposition of Model-based Learning Problems"
    - "Bayesian Finite Mixtures for Nonlinear Modeling of Educational data"
    - "Update rules for parameter estimation in Bayesian networks"
    - "Discovering Structure in Continuous Variables Using Bayesian Networks"
    - "Learning Belief Networks in the Presence of Missing Values and Hidden Variables"

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