human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants"):
  with members:
    - "PREDICTION GAMES AND ARCING ALGORITHMS"

Connected through hyperedge HE-2 (induced by source paper "An Empirical Evaluation of Bagging and Boosting"):
  with members:
    - "Actively Searching for an Effective Neural-Network Ensemble"
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"
    - "Experiments with a New Boosting Algorithm"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"

Connected through hyperedge HE-3 (induced by source paper "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods"):
  with members:
    - "Boosting Trees for Cost-Sensitive Classifications"
    - "An Efficient Boosting Algorithm for Combining Preferences"
    - "The out-of-bootstrap method for model averaging and selection"
    - "BOOSTING AND NAIVE BAYESIAN LEARNING"

Connected through hyperedge HE-4 (induced by source paper "Experiments with a New Boosting Algorithm"):
  with members:
    - "Boosting Trees for Cost-Sensitive Classifications"
    - "A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets"
    - "Pruning Adaptive Boosting ICML-97 Final Draft"
    - "Adaptive Boosting of Neural Networks for Character Recognition"
    - "Submitted to the Future Generation Computer Systems special issue on Data Mining. Using Neural Networks"
    - "Achieving High-Accuracy Text-to-Speech with Machine Learning"
    - "An Empirical Evaluation of Bagging and Boosting"
    - "Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a"

Question: Please tell me which class the center node 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