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: "Error-Correcting Output Codes for Local Learners"
  Abstract: Error-correcting output codes (ECOCs) represent classes with a set of output bits, where each bit encodes a binary classification task corresponding to a unique partition of the classes. Algorithms that use ECOCs learn the function corresponding to each bit, and combine them to generate class predictions. ECOCs can reduce both variance and bias errors for multiclass classification tasks when the errors made at the output bits are not correlated. They work well with algorithms that eagerly induce global classifiers (e.g., C4.5) but do not assist simple local classifiers (e.g., nearest neighbor), which yield correlated predictions across the output bits. We show that the output bit predictions of local learners can be decorrelated by selecting different features for each bit. We present promising empirical results for this combination of ECOCs, near est neighbor, and feature 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"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"
    - "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"
    - "Probability Estimation via Error-Correcting Output Coding"

Connected through hyperedge HE-2 (induced by source paper "Bias Plus Variance Decomposition for Zero-One Loss Functions"):
  with members:
    - "On learning hierarchical classifications"
    - "The Utility of Feature Weighting in Nearest-Neighbor Algorithms"
    - "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms"
    - "Characterizing the generalization performance of model selection strategies"
    - "Bias, variance and prediction error for classification rules"
    - "Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a"

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