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: "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.

Connected through hyperedge HE-1 (induced by source paper "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"):
  with members:
    - "On learning hierarchical classifications"
    - "Model selection using measure functions"
    - "Automatic Parameter Selection by Minimizing Estimated Error"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"
    - "Characterizing the generalization performance of model selection strategies"
    - "A New Metric-Based Approach to Model Selection"
    - "Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting"

Connected through hyperedge HE-2 (induced by source paper "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier"):
  with members:
    - "BOOSTING AND NAIVE BAYESIAN LEARNING"
    - "On learning hierarchical classifications"
    - "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 "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid"):
  with members:
    - "Pessimistic decision tree pruning based on tree size"

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