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: "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods"
  Abstract: One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show that techniques used in the analysis of Vapnik's support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins of the training examples. Finally, we compare our explanation to those based on the bias-variance decomposition.

Connected through hyperedge HE-1 (induced by source paper "The out-of-bootstrap method for model averaging and selection"):
  with members:
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"
    - "Bias, variance and prediction error for classification rules"

Connected through hyperedge HE-2 (induced by source paper "An Efficient Boosting Algorithm for Combining Preferences"):
  with members:
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Learning to Order Things"
    - "Improved Boosting Algorithms Using Confidence-rated Predictions"

Connected through hyperedge HE-3 (induced by source paper "Boosting Trees for Cost-Sensitive Classifications"):
  with members:
    - "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"
    - "Experiments with a New Boosting Algorithm"

Connected through hyperedge HE-4 (induced by source paper "Improving Bagging Performance by Increasing Decision Tree Diversity"):
  with members:
    - "Experiments with a New Boosting Algorithm"
    - "An Empirical Evaluation of Bagging and Boosting"
    - "An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants"

Connected through hyperedge HE-5 (induced by source paper "BOOSTING AND NAIVE BAYESIAN LEARNING"):
  with members:
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Building Classifiers using Bayesian Networks"
    - "Supervised and Unsupervised Discretization of Continuous Features"
    - "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: Theory