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 "PREDICTION GAMES AND ARCING ALGORITHMS"):
  Abstract: Technical Report 504 December 19, 1997 Statistics Department University of California Berkeley, CA. (4720 Abstract The theory behind the success of adaptive reweighting and combining algorithms (arcing) such as Adaboost (Freund and Schapire [1995].[1996]) and others in reducing generalization error has not been well understood. By formulating prediction, both classification and regression, as a game where one player makes a selection from instances in the training set and the other a convex linear combination of predictors from a finite set, existing arcing algorithms are shown to be algorithms for finding good game strategies. An optimal game strategy finds a combined predictor that minimizes the maximum of the error over the training set. A bound on the generalization error for the combined predictors in terms of their maximum error is proven that is sharper than bounds to date. Arcing algorithms are described that converge to the optimal strategy. Schapire et.al. [1997] offered an explanation of why Adaboost works in terms of its ability to reduce the margin. Comparing Adaboost to our optimal arcing algorithm shows that their explanation is not valid and that the answer lies elsewhere. In this situation the VC-type bounds are misleading. Some empirical results are given to explore the situation.

Includes members:
  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-1 (induced by source paper "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree")
  Member "An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Improving Bagging Performance by Increasing Decision Tree Diversity")

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