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: "Bias Plus Variance Decomposition for Zero-One Loss Functions"
  Abstract: We present a bias-variance decomposition of expected misclassification rate, the most commonly used loss function in supervised classification learning. The bias-variance decomposition for quadratic loss functions is well known and serves as an important tool for analyzing learning algorithms, yet no decomposition was offered for the more commonly used zero-one (misclassification) loss functions until the recent work of Kong & Dietterich (1995) and Breiman (1996). Their decomposition suffers from some major shortcomings though (e.g., potentially negative variance), which our decomposition avoids. We show that, in practice, the naive frequency-based estimation of the decomposition terms is by itself biased and show how to correct for this bias. We illustrate the decomposition on various algorithms and datasets from the UCI repository.

Connected through hyperedge HE-1 (induced by source paper "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms"):
  with members:
    - "A Statistical Approach to Decision Tree Modeling"
    - "Decision Tree Induction: How Effective is the Greedy Heuristic?"
    - "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"

Connected through hyperedge HE-2 (induced by source paper "The Utility of Feature Weighting in Nearest-Neighbor Algorithms"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"

Connected through hyperedge HE-3 (induced by source paper "Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a"):
  with members:
    - "Experiments with a New Boosting Algorithm"
    - "Bayesian Model Averaging"

Connected through hyperedge HE-4 (induced by source paper "Error-Correcting Output Codes for Local Learners"):
  with members:
    - "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"

Connected through hyperedge HE-5 (induced by source paper "Characterizing the generalization performance of model selection strategies"):
  with members:
    - "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"
    - "A New Metric-Based Approach to Model Selection"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"

Connected through hyperedge HE-6 (induced by source paper "On learning hierarchical classifications"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
    - "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier"

Connected through hyperedge HE-7 (induced by source paper "Bias, variance and prediction error for classification rules"):
  with members:
    - "The out-of-bootstrap method for model averaging and selection"
    - "Parametric regression 1.1 Learning problem model f bw b w in turn is an estimator"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"
    - "An Efficient Method To Estimate Bagging's Generalization Error"
    - "82 Lag-space estimation in time-series modelling keep track of cases where the estimation of P"

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: Theory