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 "Pruning Adaptive Boosting ICML-97 Final Draft"):
  Abstract: The boosting algorithm AdaBoost, developed by Freund and Schapire, has exhibited outstanding performance on several benchmark problems when using C4.5 as the "weak" algorithm to be "boosted." Like other ensemble learning approaches, AdaBoost constructs a composite hypothesis by voting many individual hypotheses. In practice, the large amount of memory required to store these hypotheses can make ensemble methods hard to deploy in applications. This paper shows that by selecting a subset of the hypotheses, it is possible to obtain nearly the same levels of performance as the entire set. The results also provide some insight into the behavior of AdaBoost.

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 "Experiments with a New Boosting Algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Achieving High-Accuracy Text-to-Speech with Machine Learning")

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