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: "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
  Abstract: We review accuracy estimation methods and compare the two most common methods: cross-validation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leave-one-out cross-validation. We report on a large-scale experiment|over half a million runs of C4.5 and a Naive-Bayes algorithm|to estimate the effects of different parameters on these algorithms on real-world datasets. For cross-validation, we vary the number of folds and whether the folds are stratified or not; for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, the best method to use for model selection is ten-fold stratified cross validation, even if computation power allows using more folds.

Connected through hyperedge HE-1 (induced by source paper "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"):
  with members:
    - "The out-of-bootstrap method for model averaging and selection"
    - "Classifying Seismic Signals by Integrating Ensembles of Neural Networks"
    - "Flexible Metric Nearest Neighbor Classiflcation"
    - "Combining estimates in regression and classification"
    - "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 "MLC A Machine Learning Library in C"):
  with members:
    - "Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology"
    - "Error-Based and Entropy-Based Discretization of Continuous Features"
    - "Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting"
    - "Applying a Machine Learning Workbench: Experience with Agricultural Databases"

Connected through hyperedge HE-3 (induced by source paper "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"):
  with members:
    - "Towards a Better Understanding of Memory-Based Reasoning Systems"
    - "Induction of One-Level Decision Trees"
    - "Computational Learning in Humans and Machines"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"
    - "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier"

Connected through hyperedge HE-4 (induced by source paper "Model selection using measure functions"):
  with members:
    - "Evaluation and Selection of Biases in Machine Learning"
    - "Beyond predictive accuracy: what?"

Connected through hyperedge HE-5 (induced by source paper "Characterizing the generalization performance of model selection strategies"):
  with members:
    - "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"
    - "A New Metric-Based Approach to Model Selection"
    - "Bias Plus Variance Decomposition for Zero-One Loss Functions"

Connected through hyperedge HE-6 (induced by source paper "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"):
  with members:
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"
    - "Toward Efficient Agnostic Learning"
    - "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"
    - "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with"

Connected through hyperedge HE-7 (induced by source paper "Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting"):
  with members:
    - "MLC A Machine Learning Library in C"

Connected through hyperedge HE-8 (induced by source paper "On learning hierarchical classifications"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Bias Plus Variance Decomposition for Zero-One Loss Functions"
    - "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier"

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