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 "Bias, variance and prediction error for classification rules"):
  Abstract: We study the notions of bias and variance for classification rules. Following Efron (1978) we develop a decomposition of prediction error into its natural components. Then we derive bootstrap estimates of these components and illustrate how they can be used to describe the error behaviour of a classifier in practice. In the process we also obtain a bootstrap estimate of the error of a "bagged" classifier.

Includes members:
  Member "The out-of-bootstrap method for model averaging and 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 "Parametric regression 1.1 Learning problem model f bw b w in turn is an estimator":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2")
  Member "Bias Plus Variance Decomposition for Zero-One Loss Functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms")
  Member "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection")
  Member "An Efficient Method To Estimate Bagging's Generalization Error":
    also connected through other hyperedges:
      (none)
  Member "82 Lag-space estimation in time-series modelling keep track of cases where the estimation of P":
    also connected through other hyperedges:
      (none)

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