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 "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.

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 "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree")
  Member "Building Classifiers using Bayesian Networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning Limited Dependence Bayesian Classifiers")
  Member "Supervised and Unsupervised Discretization of Continuous Features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Error-Based and Entropy-Based Discretization of Continuous Features")
  Member "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier")

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