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: "The out-of-bootstrap method for model averaging and selection"
  Abstract: We propose a bootstrap-based method for model averaging and selection that focuses on training points that are left out of individual bootstrap samples. This information can be used to estimate optimal weighting factors for combining estimates from different bootstrap samples, and also for finding the best subsets the linear model setting. These proposals provide alternatives to Bayesian approaches to model averaging and selection, requiring less computation and fewer subjective choices.

Connected through hyperedge HE-1 (induced by source paper "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"):
  with members:
    - "Classifying Seismic Signals by Integrating Ensembles of Neural Networks"
    - "Flexible Metric Nearest Neighbor Classiflcation"
    - "Combining estimates in regression and classification"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
    - "Bias, variance and prediction error for classification rules"
    - "The covariance inflation criterion for adaptive model selection"
    - "Unbiased Assessment of Learning Algorithms"
    - "Estimating the Accuracy of Learned Concepts"

Connected through hyperedge HE-2 (induced by source paper "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods"):
  with members:
    - "Improving Bagging Performance by Increasing Decision Tree Diversity"
    - "Boosting Trees for Cost-Sensitive Classifications"
    - "An Efficient Boosting Algorithm for Combining Preferences"
    - "BOOSTING AND NAIVE BAYESIAN LEARNING"

Connected through hyperedge HE-3 (induced by source paper "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - "Stacked Density Estimation"
    - "Bayesian Graphical Models for Discrete Data"
    - "Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance"
    - "Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology 1"
    - "Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models"
    - "Parallel Markov chain Monte Carlo sampling."
    - "Decomposable graphical Gaussian model determination"

Connected through hyperedge HE-4 (induced by source paper "Bias, variance and prediction error for classification rules"):
  with members:
    - "Parametric regression 1.1 Learning problem model f bw b w in turn is an estimator"
    - "Bias Plus Variance Decomposition for Zero-One Loss Functions"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"
    - "An Efficient Method To Estimate Bagging's Generalization Error"
    - "82 Lag-space estimation in time-series modelling keep track of cases where the estimation of P"

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