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: "BOOSTING AND NAIVE BAYESIAN LEARNING"
  Abstract: Although so-called naive Bayesian classification makes the unrealistic assumption that the values of the attributes of an example are independent given the class of the example, this learning method is remarkably successful in practice, and no uniformly better learning method is known. Boosting is a general method of combining multiple classifiers due to Yoav Freund and Rob Schapire. This paper shows that boosting applied to naive Bayesian classifiers yields combination classifiers that are representationally equivalent to standard feedforward multilayer perceptrons. (An ancillary result is that naive Bayesian classification is a nonparametric, nonlinear generalization of logistic regression.) As a training algorithm, boosted naive Bayesian learning is quite different from backpropagation, and has definite advantages. Boosting requires only linear time and constant space, and hidden nodes are learned incrementally, starting with the most important. On the real-world datasets on which the method has been tried so far, generalization performance is as good as or better than the best published result using any other learning method. Unlike all other standard learning algorithms, naive Bayesian learning, with and without boosting, can be done in logarithmic time with a linear number of parallel computing units. Accordingly, these learning methods are highly plausible computationally as models of animal learning. Other arguments suggest that they are plausible behaviorally also.

Connected through hyperedge HE-1 (induced by source paper "Building Classifiers using Bayesian Networks"):
  with members:
    - "Learning Limited Dependence Bayesian Classifiers"

Connected through hyperedge HE-2 (induced by source paper "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier"):
  with members:
    - "On learning hierarchical classifications"
    - "Lazy Bayesian Trees"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"
    - "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier"

Connected through hyperedge HE-3 (induced by source paper "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods"):
  with members:
    - "Improving Bagging Performance by Increasing Decision Tree Diversity"
    - "Boosting Trees for Cost-Sensitive Classifications"
    - "An Efficient Boosting Algorithm for Combining Preferences"
    - "The out-of-bootstrap method for model averaging and selection"

Connected through 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"):
  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"
    - "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"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"

Connected through hyperedge HE-5 (induced by source paper "Supervised and Unsupervised Discretization of Continuous Features"):
  with members:
    - "Error-Based and Entropy-Based Discretization of Continuous Features"
    - "Machine Learning and Inference"
    - "Machine Learning and Inference"

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