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 "Experiments with a New Boosting Algorithm"):
  Abstract: In an earlier paper, we introduced a new boosting algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the related notion of a pseudo-loss which is a method for forcing a learning algorithm of multi-label concepts to concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems. We performed two sets of experiments. The first set compared boosting to Breiman's bagging method when used to aggregate various classifiers (including decision trees and single attribute-value tests). We compared the performance of the two methods on a collection of machine-learning benchmarks. In the second set of experiments, we studied in more detail the performance of boosting using a nearest-neighbor classifier on an OCR problem.

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 "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods")
  Member "A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Selection of Relevant Features and Examples in Machine Learning")
  Member "Pruning Adaptive Boosting ICML-97 Final Draft":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule")
  Member "Adaptive Boosting of Neural Networks for Character Recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Constraint Tangent Distance for On-line Character Recognition")
  Member "Submitted to the Future Generation Computer Systems special issue on Data Mining. Using Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Extracting Comprehensible Concept Representations from Trained Neural Networks")
  Member "Achieving High-Accuracy Text-to-Speech with Machine Learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping")
  Member "An Empirical Evaluation of Bagging and Boosting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural 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