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 "Combining estimates in regression and classification"):
  Abstract: We consider the problem of how to combine a collection of general regression fit vectors in order to obtain a better predictive model. The individual fits may be from subset linear regression, ridge regression, or something more complex like a neural network. We develop a general framework for this problem and examine a recent cross-validation-based proposal called "stacking" in this context. Combination methods based on the bootstrap and analytic methods are also derived and compared in a number of examples, including best subsets regression and regression trees. Finally, we apply these ideas to classification problems where the estimated combination weights can yield insight into the structure of the problem.

Includes members:
  Member "Classifying Seismic Signals by Integrating Ensembles of Neural Networks":
    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 "A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Selection of Relevant Features and Examples in Machine Learning")
  Member "Stacked Density Estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Model Selection and Accounting for Model Uncertainty in Linear Regression Models")
  Member "Rule-based Machine Learning Methods for Functional Prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Structural Regression Trees")
  Member "Error-Correcting Output Coding Corrects Bias and Variance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Using Decision Trees to Improve Case-Based Learning")
  Member "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection")

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