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: "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.

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"
    - "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"
    - "Preventing "Overfitting" of Cross-Validation Data"

Connected through hyperedge HE-2 (induced by source paper "Preventing "Overfitting" of Cross-Validation Data"):
  with members:
    - "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with"

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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "Characterizing the generalization performance of model selection strategies"):
  with members:
    - "A New Metric-Based Approach to Model Selection"
    - "Bias Plus Variance Decomposition for Zero-One Loss Functions"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"

Connected through hyperedge HE-5 (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"
    - "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"

Connected through hyperedge HE-6 (induced by source paper "Toward Efficient Agnostic Learning"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Learning in the Presence of Malicious Errors"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Prediction, Learning, Uniform Convergence, and Scale-sensitive Dimensions"
    - "Tracking Drifting Concepts By Minimizing Disagreements"
    - "On Learning Conjunctions with Malicious Noise"
    - "Learning Switching Concepts"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"

Connected through hyperedge HE-7 (induced by source paper "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization"):
  with members:
    - "Pessimistic decision tree pruning based on tree size"
    - "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree"
    - "On the Boosting Ability of Top-Down Decision Tree Learning Algorithms provably optimal for decision tree"

Connected through hyperedge HE-8 (induced by source paper "A New Metric-Based Approach to Model Selection"):
  with members:
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"
    - "Characterizing the generalization performance of model selection strategies"
    - "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