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: "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.

Connected through hyperedge HE-1 (induced by source paper "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"):
  with members:
    - "Learning Sparse Perceptrons"
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "An Efficient Boosting Algorithm for Combining Preferences"
    - "BOOSTING AND NAIVE BAYESIAN LEARNING"
    - "Actively Searching for an Effective Neural-Network Ensemble"
    - "Learning to Order Things"
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "Context-sensitive learning methods for text categorization"

Connected through hyperedge HE-2 (induced by source paper "Experiments with a New Boosting Algorithm"):
  with members:
    - "Improving Bagging Performance by Increasing Decision Tree Diversity"
    - "Boosting Trees for Cost-Sensitive Classifications"
    - "A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets"
    - "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