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: "A comparison of Anapron with seven other name-pronunciation systems"
  Abstract: This paper presents an experiment comparing a new name-pronunciation system, Anapron, with seven existing systems: three state-of-the-art commercial systems (from Bellcore, Bell Labs, and DEC), two variants of a machine-learning system (NETtalk), and two humans. Anapron works by combining rule-based and case-based reasoning. It is based on the idea that it is much easier to improve a rule-based system by adding case-based reasoning to it than by tuning the rules to deal with every exception. In the experiment described here, Anapron used a set of rules adapted from MITalk and elementary foreign-language textbooks, and a case library of 5000 names. With these components | which required relatively little knowledge engineering | Anapron was found to perform almost at the level of the commercial systems, and significantly better than the two versions of NETtalk. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories of Cambridge, Massachusetts; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories. All rights reserved.

Connected through hyperedge HE-1 (induced by source paper "The evaluation of Anapron: A case study in evaluating a case-based system"):
  with members:
    - "Improving accuracy by combining rule-based and case-based reasoning"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"

Connected through hyperedge HE-2 (induced by source paper "Improving accuracy by combining rule-based and case-based reasoning"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"
    - "Case-based Acquisition of User Preferences for Solution Improvement in Ill-Structured Domains"
    - "The evaluation of Anapron: A case study in evaluating a case-based system"

Connected through hyperedge HE-3 (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"

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: Case_Based