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 covariance inflation criterion for adaptive model selection"
  Abstract: We propose a new criterion for model selection in prediction problems. The covariance inflation criterion adjusts the training error by the average covariance of the predictions and responses, when the prediction rule is applied to permuted versions of the dataset. This criterion can be applied to general prediction problems (for example regression or classification), and to general prediction rules (for example stepwise regression, tree-based models and neural nets). As a byproduct we obtain a measure of the effective number of parameters used by an adaptive procedure. We relate the covariance inflation criterion to other model selection procedures and illustrate its use in some regression and classification problems. We also revisit the conditional bootstrap approach to model selection.

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"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
    - "Bias, variance and prediction error for classification rules"
    - "Unbiased Assessment of Learning Algorithms"
    - "Estimating the Accuracy of Learned Concepts"

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