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: "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"
  Abstract: A training set of data has been used to construct a rule for predicting future responses. What is the error rate of this rule? The traditional answer to this question is given by cross-validation. The cross-validation estimate of prediction error is nearly unbiased, but can be highly variable. This article discusses bootstrap estimates of prediction error, which can be thought of as smoothed versions of cross-validation. A particular bootstrap method, the 632+ rule, is shown to substantially outperform cross-validation in a catalog of 24 simulation experiments. Besides providing point estimates, we also consider estimating the variability of an error rate estimate. All of the results here are nonparametric, and apply to any possible prediction rule: however we only study classification problems with 0-1 loss in detail. Our simulations include "smooth" prediction rules like Fisher's Linear Discriminant Function, and unsmooth ones like Nearest Neighbors.

Connected through hyperedge HE-1 (induced by source paper "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"):
  with members:
    - "On learning hierarchical classifications"
    - "Model selection using measure functions"
    - "Automatic Parameter Selection by Minimizing Estimated Error"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"
    - "Characterizing the generalization performance of model selection strategies"
    - "A New Metric-Based Approach to Model Selection"
    - "Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting"

Connected through hyperedge HE-2 (induced by source paper "Classifying Seismic Signals by Integrating Ensembles of Neural Networks"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Combining estimates in regression and classification"

Connected through hyperedge HE-3 (induced by source paper "The out-of-bootstrap method for model averaging and selection"):
  with members:
    - "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods"
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Bias, variance and prediction error for classification rules"

Connected through hyperedge HE-4 (induced by source paper "The covariance inflation criterion for adaptive model selection"):
  with members:
    (none)

Connected through hyperedge HE-5 (induced by source paper "Combining estimates in regression and classification"):
  with members:
    - "Classifying Seismic Signals by Integrating Ensembles of Neural Networks"
    - "A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets"
    - "Stacked Density Estimation"
    - "Rule-based Machine Learning Methods for Functional Prediction"
    - "Error-Correcting Output Coding Corrects Bias and Variance"

Connected through hyperedge HE-6 (induced by source paper "Flexible Metric Nearest Neighbor Classiflcation"):
  with members:
    - "Adaptive Noise Injection for Input Variables Relevance Determination"
    - "An adaptation of Relief for attribute estimation in regression"
    - "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."
    - "Virtual Seens and the Frequently Used Dataset"
    - "DART/HYESS Users Guide  recursive covering approach to local learning"
    - "Selection of Relevant Features and Examples in Machine Learning"

Connected through hyperedge HE-7 (induced by source paper "Estimating the Accuracy of Learned Concepts"):
  with members:
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
    - "On the Induction of Intelligible Ensembles"

Connected through hyperedge HE-8 (induced by source paper "Bias, variance and prediction error for classification rules"):
  with members:
    - "The out-of-bootstrap method for model averaging and selection"
    - "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"
    - "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