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 "Lazy Bayesian Trees"):
  Abstract: The naive Bayesian classifier is simple and effective, but its attribute independence assumption is often violated in the real world. A number of approaches have been developed that seek to alleviate this problem. A Bayesian tree learning algorithm builds a decision tree, and generates a local Bayesian classifier at each leaf instead of predicting a single class. However, Bayesian tree learning still suffers from the replication and fragmentation problems of tree learning. While inferred Bayesian trees demonstrate low average prediction error rates, there is reason to believe that error rates will be higher for those leaves with few training examples. This paper proposes a novel lazy Bayesian tree learning algorithm. For each test example, it conceptually builds a most appropriate Bayesian tree. In practice, only one path with a local Bayesian classifier at its leaf is created. Experiments with a wide variety of real-world and artificial domains show that this new algorithm has significantly lower overall prediction error rates than a naive Bayesian classifier, C4.5, and a Bayesian tree learning algorithm.

Includes members:
  Member "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule")
  Member "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier")
  Member "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Pessimistic decision tree pruning based on tree size")

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: Probabilistic_Methods