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: "GENERALIZATION PERFORMANCE OF BACKPROPAGATION LEARNING ON A SYLLABIFICATION TASK"
  Abstract: We investigated the generalization capabilities of backpropagation learning in feed-forward and recurrent feed-forward connectionist networks on the assignment of syllable boundaries to orthographic representations in Dutch (hyphenation). This is a difficult task because phonological and morphological constraints interact, leading to ambiguity in the input patterns. We compared the results to different symbolic pattern matching approaches, and to an exemplar-based generalization scheme, related to a k-nearest neighbour approach, but using a similarity metric weighed by the relative information entropy of positions in the training patterns. Our results indicate that the generalization performance of backpropagation learning for this task is not better than that of the best symbolic pattern matching approaches, and of exemplar-based generalization.

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"
    - "Memory-Based Lexical Acquisition and Processing"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"

Connected through hyperedge HE-2 (induced by source paper "Resolving PP attachment Ambiguities with Memory-Based Learning"):
  with members:
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "Memory-Based Lexical Acquisition and Processing"

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"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"

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"
    - "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"

Connected through hyperedge HE-5 (induced by source paper "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"):
  with members:
    - "Improving accuracy by combining rule-based and case-based reasoning"
    - "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"
    - "Fast NP Chunking Using Memory-Based Learning Techniques"
    - "Memory-Based Lexical Acquisition and Processing"
    - "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING"
    - "Automatic Phonetic Transcription of Words Based On Sparse Data"

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"
    - "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"

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: Neural_Networks