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: "From: Computational Learning Theory and Natural Systems, Chapter 18, "Cross-validation and Modal Theories", Cross-Validation and"
  Abstract: Cross-validation is a frequently used, intuitively pleasing technique for estimating the accuracy of theories learned by machine learning algorithms. During testing of a machine learning algorithm (foil) on new databases of prokaryotic RNA transcription promoters which we have developed, cross-validation displayed an interesting phenomenon. One theory is found repeatedly and is responsible for very little of the cross-validation error, whereas other theories are found very infrequently which tend to be responsible for the majority of the cross-validation error. It is tempting to believe that the most frequently found theory (the "modal theory") may be more accurate as a classifier of unseen data than the other theories. However, experiments showed that modal theories are not more accurate on unseen data than the other theories found less frequently during cross-validation. Modal theories may be useful in predicting when cross-validation is a poor estimate of true accuracy. We offer explanations 1 For correspondence: Department of Computer Science and Engineering, University of California, San

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"
    - "The covariance inflation criterion for adaptive model selection"
    - "Unbiased Assessment of Learning Algorithms"

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: Theory