human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Improving Bagging Performance by Increasing Decision Tree Diversity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants")
  Member "Boosting Trees for Cost-Sensitive Classifications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm")
  Member "An Efficient Boosting Algorithm for Combining Preferences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Improved Boosting Algorithms Using Confidence-rated Predictions")
  Member "The out-of-bootstrap method for model averaging and selection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule")
  Member "BOOSTING AND NAIVE BAYESIAN LEARNING":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Building Classifiers using Bayesian Networks")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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