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 "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"):
  Abstract: We review accuracy estimation methods and compare the two most common methods: cross-validation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leave-one-out cross-validation. We report on a large-scale experiment|over half a million runs of C4.5 and a Naive-Bayes algorithm|to estimate the effects of different parameters on these algorithms on real-world datasets. For cross-validation, we vary the number of folds and whether the folds are stratified or not; for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, the best method to use for model selection is ten-fold stratified cross validation, even if computation power allows using more folds.

Includes members:
  Member "On learning hierarchical classifications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier")
  Member "Model selection using measure functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Beyond predictive accuracy: what?")
  Member "Automatic Parameter Selection by Minimizing Estimated Error":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "The Power of Decision Tables")
  Member "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Towards a Better Understanding of Memory-Based Reasoning Systems")
  Member "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with")
  Member "Characterizing the generalization performance of model selection strategies":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the")
  Member "A New Metric-Based Approach to Model Selection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the")
  Member "Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "MLC A Machine Learning Library in C")

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