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: "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with"
  Abstract: We give an analysis of the generalization error of cross validation in terms of two natural measures of the difficulty of the problem under consideration: the approximation rate (the accuracy to which the target function can be ideally approximated as a function of the number of hypothesis parameters), and the estimation rate (the deviation between the training and generalization errors as a function of the number of hypothesis parameters). The approximation rate captures the complexity of the target function with respect to the hypothesis model, and the estimation rate captures the extent to which the hypothesis model suffers from overfitting. Using these two measures, we give a rigorous and general bound on the error of cross validation. The bound clearly shows the tradeoffs involved with making fl the fraction of data saved for testing too large or too small. By optimizing the bound with respect to fl, we then argue (through a combination of formal analysis, plotting, and controlled experimentation) that the following qualitative properties of cross validation behavior should be quite robust to significant changes in the underlying model selection problem:

Connected through hyperedge HE-1 (induced by source paper "Preventing "Overfitting" of Cross-Validation Data"):
  with members:
    - "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"

Connected through hyperedge HE-2 (induced by source paper "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"):
  with members:
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"
    - "Toward Efficient Agnostic Learning"
    - "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"
    - "Preventing "Overfitting" of Cross-Validation Data"
    - "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization"
    - "Characterizing the generalization performance of model selection strategies"
    - "A New Metric-Based Approach to Model Selection"

Connected through hyperedge HE-3 (induced by source paper "Rigorous Learning Curve Bounds from Statistical Mechanics"):
  with members:
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Self bounding learning algorithms"
    - "Markov Decision Processes in Large State Spaces"
    - "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Where Genetic Algorithms Excel"
    - "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"
    - "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"

Connected through hyperedge HE-4 (induced by source paper "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"):
  with members:
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"
    - "Toward Efficient Agnostic Learning"
    - "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"

Connected through hyperedge HE-5 (induced by source paper "Improving Regressors using Boosting Techniques"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"

Connected through hyperedge HE-6 (induced by source paper "A model f w depending on a set of parameters w is used to estimate"):
  with members:
    (none)

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