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: "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"
  Abstract: In this paper we prove sanity-check bounds for the error of the leave-one-out cross-validation estimate of the generalization error: that is, bounds showing that the worst-case error of this estimate is not much worse than that of the training error estimate. The name sanity-check refers to the fact that although we often expect the leave-one-out estimate to perform considerably better than the training error estimate, we are here only seeking assurance that its performance will not be considerably worse. Perhaps surprisingly, such assurance has been given only for rather limited cases in the prior literature on cross-validation. Any nontrivial bound on the error of leave-one-out must rely on some notion of algorithmic stability. Previous bounds relied on the rather strong notion of hypothesis stability, whose application was primarily limited to nearest-neighbor and other local algorithms. Here we introduce the new and weaker notion of error stability, and apply it to obtain sanity-check bounds for leave-one-out for other classes of learning algorithms, including training error minimization procedures and Bayesian algorithms. We also provide lower bounds demonstrating the necessity of error stability for proving bounds on the error of the leave-one-out estimate, and the fact that for training error minimization algorithms, in the worst case such bounds must still depend on the Vapnik-Chervonenkis dimension of the hypothesis class.

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"
    - "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 "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"
    - "Characterizing the generalization performance of model selection strategies"
    - "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-3 (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"
    - "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"
    - "Characterizing the generalization performance of model selection strategies"
    - "A New Metric-Based Approach to Model Selection"

Connected through hyperedge HE-4 (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"
    - "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"
    - "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with"

Connected through hyperedge HE-5 (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"

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