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: "Achieving High-Accuracy Text-to-Speech with Machine Learning"
  Abstract: In 1987, Sejnowski and Rosenberg developed their famous NETtalk system for English text-to-speech. This chapter describes a machine learning approach to text-to-speech that builds upon and extends the initial NETtalk work. Among the many extensions to the NETtalk system were the following: a different learning algorithm, a wider input "window", error-correcting output coding, a right-to-left scan of the word to be pronounced (with the results of each decision influencing subsequent decisions), and the addition of several useful input features. These changes yielded a system that performs much better than the original NETtalk system. After training on 19,002 words, the system achieves 93.7% correct pronunciation of individual phonemes and 64.8% correct pronunciation of whole words (where the pronunciation must exactly match the dictionary pronunciation to be correct). Based on the judgements of three human participants in a blind assessment study, our system was estimated to have a serious error rate of 16.7% (on whole words) compared to an error rate of 26.1% for the DECTalk3.0 rulebase.

Connected through hyperedge HE-1 (induced by source paper "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"):
  with members:
    - "Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and"
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Constructing Nominal Xof-N Attributes"
    - "Improving accuracy by combining rule-based and case-based reasoning"
    - "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Draft Symbolic Representation of Neural Networks"
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"

Connected through hyperedge HE-2 (induced by source paper "Experiments with a New Boosting Algorithm"):
  with members:
    - "Improving Bagging Performance by Increasing Decision Tree Diversity"
    - "Boosting Trees for Cost-Sensitive Classifications"
    - "A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets"
    - "Pruning Adaptive Boosting ICML-97 Final Draft"
    - "Adaptive Boosting of Neural Networks for Character Recognition"
    - "Submitted to the Future Generation Computer Systems special issue on Data Mining. Using Neural Networks"
    - "An Empirical Evaluation of Bagging and Boosting"
    - "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