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: "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"
  Abstract: We describe an approach to grapheme-to-phoneme conversion which is both language-independent and data-oriented. Given a set of examples (spelling words with their associated phonetic representation) in a language, a grapheme-to-phoneme conversion system is automatically produced for that language which takes as its input the spelling of words, and produces as its output the phonetic transcription according to the rules implicit in the training data. We describe the design of the system, and compare its performance to knowledge-based and alternative data-oriented approaches.

Connected through hyperedge HE-1 (induced by source paper "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING"):
  with members:
    - "Fast NP Chunking Using Memory-Based Learning Techniques"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "GENERALIZATION PERFORMANCE OF BACKPROPAGATION LEARNING ON A SYLLABIFICATION TASK"
    - "Memory-Based Lexical Acquisition and Processing"

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"
    - "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"
    - "A comparison of Anapron with seven other name-pronunciation systems"

Connected through hyperedge HE-3 (induced by source paper "Automatic Phonetic Transcription of Words Based On Sparse Data"):
  with members:
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "GENERALIZATION PERFORMANCE OF BACKPROPAGATION LEARNING ON A SYLLABIFICATION TASK"

Connected through hyperedge HE-4 (induced by source paper "Memory-Based Lexical Acquisition and Processing"):
  with members:
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"
    - "Resolving PP attachment Ambiguities with Memory-Based Learning"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "GENERALIZATION PERFORMANCE OF BACKPROPAGATION LEARNING ON A SYLLABIFICATION TASK"
    - "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING"

Connected through hyperedge HE-5 (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"
    - "Error-Correcting Output Codes for Local Learners"
    - "Probability Estimation via Error-Correcting Output Coding"

Connected through hyperedge HE-6 (induced by source paper "Fast NP Chunking Using Memory-Based Learning Techniques"):
  with members:
    - "Oblivious Decision Trees and Abstract Cases"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "GENERALIZATION PERFORMANCE OF BACKPROPAGATION LEARNING ON A SYLLABIFICATION TASK"
    - "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING"

Connected through hyperedge HE-7 (induced by source paper "GENERALIZATION PERFORMANCE OF BACKPROPAGATION LEARNING ON A SYLLABIFICATION TASK"):
  with members:
    - "Fast NP Chunking Using Memory-Based Learning Techniques"
    - "Resolving PP attachment Ambiguities with Memory-Based Learning"
    - "Memory-Based Lexical Acquisition and Processing"
    - "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING"
    - "Automatic Phonetic Transcription of Words Based On Sparse Data"

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