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 "Improving Regressors using Boosting Techniques"):
  Abstract: In the regression context, boosting and bagging are techniques to build a committee of regressors that may be superior to a single regressor. We use regression trees as fundamental building blocks in bagging committee machines and boosting committee machines. Performance is analyzed on three non-linear functions and the Boston housing database. In all cases, boosting is at least equivalent, and in most cases better than bagging in terms of prediction error.

Includes members:
  Member "A System for Induction of Oblique Decision Trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A Support Vector Machine Approach to Decision Trees")
  Member "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree")
  Member "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Preventing "Overfitting" of Cross-Validation Data")

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