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: "Characterizing the generalization performance of model selection strategies"
  Abstract: We investigate the structure of model selection problems via the bias/variance decomposition. In particular, we characterize the essential aspects of a model selection task by the bias and variance profiles it generates over the sequence of hypothesis classes. With this view, we develop a new understanding of complexity-penalization methods: First, the penalty terms can be interpreted as postulating a particular profile for the variances as a function of model complexityif the postulated and true profiles do not match, then systematic under-fitting or over-fitting results, depending on whether the penalty terms are too large or too small. Second, we observe that it is generally best to penalize according to the true variances of the task, and therefore no fixed penalization strategy is optimal across all problems. We then use this characterization to introduce the notion of easy versus hard model selection problems. Here we show that if the variance profile grows too rapidly in relation to the biases, then standard model selection techniques become prone to significant errors. This can happen, for example, in regression problems where the independent variables are drawn from wide-tailed distributions. To counter this, we discuss a new model selection strategy that dramatically outperforms standard complexity-penalization and hold-out meth ods on these hard tasks.

Connected through hyperedge HE-1 (induced by source paper "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"):
  with members:
    - "On learning hierarchical classifications"
    - "Model selection using measure functions"
    - "Automatic Parameter Selection by Minimizing Estimated Error"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"
    - "A New Metric-Based Approach to Model Selection"
    - "Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting"
    - "MLC A Machine Learning Library in C"

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"
    - "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with"
    - "Preventing "Overfitting" of Cross-Validation Data"
    - "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization"
    - "A New Metric-Based Approach to Model Selection"

Connected through hyperedge HE-3 (induced by source paper "Bias Plus Variance Decomposition for Zero-One Loss Functions"):
  with members:
    - "On learning hierarchical classifications"
    - "The Utility of Feature Weighting in Nearest-Neighbor Algorithms"
    - "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms"
    - "Error-Correcting Output Codes for Local Learners"
    - "Bias, variance and prediction error for classification rules"
    - "Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a"

Connected through hyperedge HE-4 (induced by source paper "A New Metric-Based Approach to Model Selection"):
  with members:
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"
    - "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"

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