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: "Probability Estimation via Error-Correcting Output Coding"
  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. In this paper, we will extend the technique so that ECOC can also provide class probability information. ECOC is a method of converting k-class supervised learning problem into a large number L of two-class supervised learning problems and then combining the results of these L evaluations. The underlying two-class supervised learning algorithms are assumed to provide L probability estimates. The problem of computing class probabilities is formulated as an over-constrained system of L linear equations. Least squares methods are applied to solve these equations. Accuracy and reliability of the probability estimates are demonstrated.

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"
    - "Error-Correcting Output Codes for Local Learners"

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