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: "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid"
  Abstract: Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classification tasks even when the conditional independence assumption on which they are based is violated. However, most studies were done on small databases. We show that in some larger databases, the accuracy of Naive-Bayes does not scale up as well as decision trees. We then propose a new algorithm, NBTree, which induces a hybrid of decision-tree classifiers and Naive-Bayes classifiers: the decision-tree nodes contain uni-variate splits as regular decision-trees, but the leaves contain Naive-Bayesian classifiers. The approach retains the interpretability of Naive-Bayes and decision trees, while resulting in classifiers that frequently outperform both constituents, especially in the larger databases tested.

Connected through hyperedge HE-1 (induced by source paper "Pessimistic decision tree pruning based on tree size"):
  with members:
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Self bounding learning algorithms"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"
    - "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization"

Connected through hyperedge HE-2 (induced by source paper "Lazy Bayesian Trees"):
  with members:
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
    - "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier"

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: Probabilistic_Methods