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: "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"

Connected through hyperedge HE-1 (induced by source paper "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"):
  with members:
    - "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"
    - "Error-Correcting Output Coding Corrects Bias and Variance"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"
    - "Error-Correcting Output Codes for Local Learners"
    - "Probability Estimation via Error-Correcting Output Coding"

Connected through hyperedge HE-2 (induced by source paper "A System for Induction of Oblique Decision Trees"):
  with members:
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "A hierarchical ensemble of decision trees applied to classifying data from a psychological experiment"
    - "Lazy Induction Triggered by CBR"
    - "Induction of Oblique Decision Trees"
    - "Geometry in Learning"
    - "Lookahead and Pathology in Decision Tree Induction"
    - "A Decision Tree System for Finding Genes in DNA"
    - "Decision Tree Induction: How Effective is the Greedy Heuristic?"

Connected through hyperedge HE-3 (induced by source paper "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"):
  with members:
    - "Learning Sparse Perceptrons"
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "An Efficient Boosting Algorithm for Combining Preferences"
    - "BOOSTING AND NAIVE BAYESIAN LEARNING"
    - "Actively Searching for an Effective Neural-Network Ensemble"
    - "Learning to Order Things"
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "Context-sensitive learning methods for text categorization"

Connected through hyperedge HE-4 (induced by source paper "Generating Accurate and Diverse Members of a Neural-Network Ensemble"):
  with members:
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"
    - "An Empirical Evaluation of Bagging and Boosting"
    - "A New Metric-Based Approach to Model Selection"
    - "Using Neural Networks to Automatically Refine Expert System Knowledge Bases: Experiments in the NYNEX MAX Domain"

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