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 Bagging Performance by Increasing Decision Tree Diversity"):
  Abstract: Ensembles of decision trees often exhibit greater predictive accuracy than single trees alone. Bagging and boosting are two standard ways of generating and combining multiple trees. Boosting has been empirically determined to be the more effective of the two, and it has recently been proposed that this may be because it produces more diverse trees than bagging. This paper reports empirical findings that strongly support this hypothesis. We enforce greater decision tree diversity in bagging by a simple modification of the underlying decision tree learner that utilizes randomly-generated decision stumps of predefined depth as the starting point for tree induction. The modified procedure yields very competitive results while still retaining one of the attractive properties of bagging: all iterations are independent. Additionally, we also investigate a possible integration of bagging and boosting. All these ensemble-generating procedures are compared empirically on various domains.

Includes members:
  Member "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "The out-of-bootstrap method for model averaging and selection")
  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")
  Member "An Empirical Evaluation of Bagging and Boosting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks")
  Member "An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "PREDICTION GAMES AND ARCING ALGORITHMS")

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