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: "Comparing Predictive Inference Methods for Discrete Domains"
  Abstract: Predictive inference is seen here as the process of determining the predictive distribution of a discrete variable, given a data set of training examples and the values for the other problem domain variables. We consider three approaches for computing this predictive distribution, and assume that the joint probability distribution for the variables belongs to a set of distributions determined by a set of parametric models. In the simplest case, the predictive distribution is computed by using the model with the maximum a posteriori (MAP) posterior probability. In the evidence approach, the predictive distribution is obtained by averaging over all the individual models in the model family. In the third case, we define the predictive distribution by using Rissanen's new definition of stochastic complexity. Our experiments performed with the family of Naive Bayes models suggest that when using all the data available, the stochastic complexity approach produces the most accurate predictions in the log-score sense. However, when the amount of available training data is decreased, the evidence approach clearly outperforms the two other approaches. The MAP predictive distribution is clearly inferior in the log-score sense to the two more sophisticated approaches, but for the 0/1-score the MAP approach may still in some cases produce the best results.

Connected through hyperedge HE-1 (induced by source paper "Comparing Bayesian Model Class Selection Criteria by Discrete Finite Mixtures"):
  with members:
    - "Bayesian Finite Mixtures for Nonlinear Modeling of Educational data"

Connected through hyperedge HE-2 (induced by source paper "Probabilistic Instance-Based Learning"):
  with members:
    - "Using Neural Networks for Descriptive Statistical Analysis of Educational Data"

Connected through hyperedge HE-3 (induced by source paper "Constructing Bayesian finite mixture models by the EM algorithm"):
  with members:
    - "Estimating Dependency Structure as a Hidden Variable"

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