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: "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.

Connected through hyperedge HE-1 (induced by source paper "Rule-based Machine Learning Methods for Functional Prediction"):
  with members:
    - "Structural Regression Trees"
    - "Search-based Class Discretization"

Connected through hyperedge HE-2 (induced by source paper "Classifying Seismic Signals by Integrating Ensembles of Neural Networks"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"

Connected through hyperedge HE-3 (induced by source paper "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"):
  with members:
    - "The out-of-bootstrap method for model averaging and selection"
    - "Classifying Seismic Signals by Integrating Ensembles of Neural Networks"
    - "Flexible Metric Nearest Neighbor Classiflcation"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
    - "Bias, variance and prediction error for classification rules"
    - "The covariance inflation criterion for adaptive model selection"
    - "Unbiased Assessment of Learning Algorithms"
    - "Estimating the Accuracy of Learned Concepts"

Connected through hyperedge HE-4 (induced by source paper "A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Experiments with a New Boosting Algorithm"
    - "Selection of Relevant Features and Examples in Machine Learning"

Connected through hyperedge HE-5 (induced by source paper "Error-Correcting Output Coding Corrects Bias and Variance"):
  with members:
    - "Using Decision Trees to Improve Case-Based Learning"
    - "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"

Connected through hyperedge HE-6 (induced by source paper "Stacked Density Estimation"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"

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