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: "SINGLE FACTOR ANALYSIS BY MML ESTIMATION"
  Abstract: The Minimum Message Length (MML) technique is applied to the problem of estimating the parameters of a multivariate Gaussian model in which the correlation structure is modelled by a single common factor. Implicit estimator equations are derived and compared with those obtained from a Maximum Likelihood (ML) analysis. Unlike ML, the MML estimators remain consistent when used to estimate both the factor loadings and factor scores. Tests on simulated data show the MML estimates to be on av erage more accurate than the ML estimates when the former exist. If the data show little evidence for a factor, the MML estimate collapses. It is shown that the condition for the existence of an MML estimate is essentially that the log likelihood ratio in favour of the factor model exceed the value expected under the null (no-factor) hypotheses.

Connected through hyperedge HE-1 (induced by source paper "MML mixture modelling of multi-state, Poisson, von Mises circular and Gaussian distributions"):
  with members:
    - "Finding Overlapping Distributions with MML"
    - "Unsupervised Learning Using MML"
    - "BAYESIAN ESTIMATION OF THE VON MISES CONCENTRATION PARAMETER"

Connected through hyperedge HE-2 (induced by source paper "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Finding Overlapping Distributions with MML"
    - "On Pruning and Averaging Decision Trees"
    - "Unsupervised Learning Using MML"
    - "BAYESIAN ESTIMATION OF THE VON MISES CONCENTRATION PARAMETER"
    - "Bayesian and Information-Theoretic Priors for Bayesian Network Parameters"
    - "Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data"

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