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 "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.

Includes members:
  Member "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with")
  Member "A New Metric-Based Approach to Model Selection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection")
  Member "Bias Plus Variance Decomposition for Zero-One Loss Functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms")
  Member "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule")

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