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 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.

Includes members:
  Member "On learning hierarchical classifications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection")
  Member "The Utility of Feature Weighting in Nearest-Neighbor Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features")
  Member "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Decision Tree Induction: How Effective is the Greedy Heuristic?")
  Member "Characterizing the generalization performance of model selection strategies":
    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 "Error-Correcting Output Codes for Local Learners":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs")
  Member "Bias, variance and prediction error for classification rules":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "An Efficient Method To Estimate Bagging's Generalization Error")
  Member "Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Bayesian Model Averaging")

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: Theory