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: "Preventing "Overfitting" of Cross-Validation Data"
  Abstract: Suppose that, for a learning task, we have to select one hypothesis out of a set of hypotheses (that may, for example, have been generated by multiple applications of a randomized learning algorithm). A common approach is to evaluate each hypothesis in the set on some previously unseen cross-validation data, and then to select the hypothesis that had the lowest cross-validation error. But when the cross-validation data is partially corrupted such as by noise, and if the set of hypotheses we are selecting from is large, then "folklore" also warns about "overfitting" the cross-validation data [Klockars and Sax, 1986, Tukey, 1949, Tukey, 1953]. In this paper, we explain how this "overfitting" really occurs, and show the surprising result that it can be overcome by selecting a hypothesis with a higher cross-validation error, over others with lower cross-validation errors. We give reasons for not selecting the hypothesis with the lowest cross-validation error, and propose a new algorithm, LOOCVCV, that uses a computa-tionally efficient form of leave-one-out cross-validation to select such a hypothesis. Finally, we present experimental results for one domain, that show LOOCVCV consistently beating picking the hypothesis with the lowest cross-validation error, even when using reasonably large cross-validation sets.

Connected through hyperedge HE-1 (induced by source paper "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with"):
  with members:
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"
    - "Improving Regressors using Boosting Techniques"
    - "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"
    - "A model f w depending on a set of parameters w is used to estimate"

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"
    - "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"

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