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: "Automatic Parameter Selection by Minimizing Estimated Error"
  Abstract: We address the problem of finding the parameter settings that will result in optimal performance of a given learning algorithm using a particular dataset as training data. We describe a "wrapper" method, considering determination of the best parameters as a discrete function optimization problem. The method uses best-first search and cross-validation to wrap around the basic induction algorithm: the search explores the space of parameter values, running the basic algorithm many times on training and holdout sets produced by cross-validation to get an estimate of the expected error of each parameter setting. Thus, the final selected parameter settings are tuned for the specific induction algorithm and dataset being studied. We report experiments with this method on 33 datasets selected from the UCI and StatLog collections using C4.5 as the basic induction algorithm. At a 90% confidence level, our method improves the performance of C4.5 on nine domains, degrades performance on one, and is statistically indistinguishable from C4.5 on the rest. On the sample of datasets used for comparison, our method yields an average 13% relative decrease in error rate. We expect to see similar performance improvements when using our method with other machine learning al gorithms.

Connected through hyperedge HE-1 (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"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"
    - "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-2 (induced by source paper "The Power of Decision Tables"):
  with members:
    - "Compression-Based Feature Subset Selection  Keywords: Minimum Description Length Principle, Cross Validation, Noise"
    - "Induction of Condensed Determinations"
    - "Decision Tree Induction Based on Efficient Tree Restructuring"
    - "Targeting Business Users with Decision Table Classifiers"
    - "A Yardstick for the Evaluation of Case-Based Classifiers"

Connected through hyperedge HE-3 (induced by source paper "Feature Subset Selection as Search with Probabilistic Estimates"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology"

Connected through hyperedge HE-4 (induced by source paper "Irrelevant Features and the Subset Selection Problem"):
  with members:
    - "Theory Revision in Fault Hierarchies"
    - "An Optimized Theory Revision Module"
    - "Expectation-Based Selective Attention for Visual Monitoring and Control of a Robot Vehicle"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "NP-Completeness of Searches for Smallest Possible Feature Sets a subset of the set of all"
    - "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome"

Connected through hyperedge HE-5 (induced by source paper "Learning Symbolic Rules Using Artificial Neural Networks"):
  with members:
    - "Using Sampling and Queries to Extract Rules from Trained Neural Networks"
    - "Submitted to the Future Generation Computer Systems special issue on Data Mining. Using Neural Networks"
    - "Machine learning in prognosis of the femoral neck fracture recovery examples, estimating attributes, explanation ability,"

Connected through hyperedge HE-6 (induced by source paper "Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Structural Regression Trees"
    - "Knowledge Discovery in International Conflict Databases"
    - "Beyond Correlation: Bringing Artificial Intelligence to Events Data"

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