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 Coding Corrects Bias and Variance"
  Abstract: Previous research has shown that a technique called error-correcting output coding (ECOC) can dramatically improve the classification accuracy of supervised learning algorithms that learn to classify data points into one of k 2 classes. This paper presents an investigation of why the ECOC technique works, particularly when employed with decision-tree learning algorithms. It shows that the ECOC method| like any form of voting or committee|can reduce the variance of the learning algorithm. Furthermore|unlike methods that simply combine multiple runs of the same learning algorithm|ECOC can correct for errors caused by the bias of the learning algorithm. Experiments show that this bias correction ability relies on the non-local be havior of C4.5.

Connected through hyperedge HE-1 (induced by source paper "Using Decision Trees to Improve Case-Based Learning"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Case-Based Sonogram Classification"
    - "Learning to Refine Case Libraries:"
    - "Oblivious Decision Trees and Abstract Cases"
    - "Refining Conversational Case Libraries"

Connected through hyperedge HE-2 (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"
    - "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"
    - "Error-Correcting Output Codes for Local Learners"
    - "Probability Estimation via Error-Correcting Output Coding"

Connected through hyperedge HE-3 (induced by source paper "Combining estimates in regression and classification"):
  with members:
    - "Classifying Seismic Signals by Integrating Ensembles of Neural Networks"
    - "A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets"
    - "Stacked Density Estimation"
    - "Rule-based Machine Learning Methods for Functional Prediction"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"

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