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: "Constructing Bayesian finite mixture models by the EM algorithm"
  Abstract: Email: Firstname.Lastname@cs.Helsinki.FI Report C-1996-9, University of Helsinki, Department of Computer Science. Abstract In this paper we explore the use of finite mixture models for building decision support systems capable of sound probabilistic inference. Finite mixture models have many appealing properties: they are computationally efficient in the prediction (reasoning) phase, they are universal in the sense that they can approximate any problem domain distribution, and they can handle multimod-ality well. We present a formulation of the model construction problem in the Bayesian framework for finite mixture models, and describe how Bayesian inference is performed given such a model. The model construction problem can be seen as missing data estimation and we describe a realization of the Expectation-Maximization (EM) algorithm for finding good models. To prove the feasibility of our approach, we report crossvalidated empirical results on several publicly available classification problem datasets, and compare our results to corresponding results obtained by alternative techniques, such as neural networks and decision trees. The comparison is based on the best results reported in the literature on the datasets in question. It appears that using the theoretically sound Bayesian framework suggested here the other reported results can be outperformed with a relatively small effort.

Connected through hyperedge HE-1 (induced by source paper "Comparing Predictive Inference Methods for Discrete Domains"):
  with members:
    - "Comparing Bayesian Model Class Selection Criteria by Discrete Finite Mixtures"
    - "Probabilistic Instance-Based Learning"

Connected through hyperedge HE-2 (induced by source paper "Estimating Dependency Structure as a Hidden Variable"):
  with members:
    (none)

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