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 "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"):
  Abstract: We investigate the problem of model selection in the setting of supervised learning of boolean functions from independent random examples. More precisely, we compare methods for finding a balance between the complexity of the hypothesis chosen and its observed error on a random training sample of limited size, when the goal is that of minimizing the resulting generalization error. We undertake a detailed comparison of three well-known model selection methods | a variation of Vapnik's Guaranteed Risk Minimization (GRM), an instance of Rissanen's Minimum Description Length Principle (MDL), and cross validation (CV). We introduce a general class of model selection methods (called penalty-based methods) that includes both GRM and MDL, and provide general methods for analyzing such rules. We provide both controlled experimental evidence and formal theorems to support the following conclusions: * The class of penalty-based methods is fundamentally handicapped in the sense that there exist two types of model selection problems for which every penalty-based method must incur large generalization error on at least one, while CV enjoys small generalization error Despite the inescapable incomparability of model selection methods under certain circumstances, we conclude with a discussion of our belief that the balance of the evidence provides specific reasons to prefer CV to other methods, unless one is in possession of detailed problem-specific information. on both.

Includes members:
  Member "Rigorous Learning Curve Bounds from Statistical Mechanics":
    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 "Toward Efficient Agnostic Learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Cognitive Computation (Extended Abstract)")
  Member "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with")
  Member "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Preventing "Overfitting" of Cross-Validation Data")
  Member "Preventing "Overfitting" of Cross-Validation Data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with")
  Member "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree")
  Member "Characterizing the generalization performance of model selection strategies":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection")
  Member "A New Metric-Based Approach to Model Selection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection")

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