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: "Bias, variance and prediction error for classification rules"
  Abstract: We study the notions of bias and variance for classification rules. Following Efron (1978) we develop a decomposition of prediction error into its natural components. Then we derive bootstrap estimates of these components and illustrate how they can be used to describe the error behaviour of a classifier in practice. In the process we also obtain a bootstrap estimate of the error of a "bagged" classifier.

Connected through hyperedge HE-1 (induced by source paper "An Efficient Method To Estimate Bagging's Generalization Error"):
  with members:
    (none)

Connected through hyperedge HE-2 (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"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
    - "The covariance inflation criterion for adaptive model selection"
    - "Unbiased Assessment of Learning Algorithms"
    - "Estimating the Accuracy of Learned Concepts"

Connected through hyperedge HE-3 (induced by source paper "Parametric regression 1.1 Learning problem model f bw b w in turn is an estimator"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"

Connected through hyperedge HE-4 (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"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"

Connected through hyperedge HE-5 (induced by source paper "Bias Plus Variance Decomposition for Zero-One Loss Functions"):
  with members:
    - "On learning hierarchical classifications"
    - "The Utility of Feature Weighting in Nearest-Neighbor Algorithms"
    - "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms"
    - "Characterizing the generalization performance of model selection strategies"
    - "Error-Correcting Output Codes for Local Learners"
    - "Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a"

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