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: "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING"
  Abstract: The need for software modules performing natural language processing (NLP) tasks is growing. These modules should perform efficiently and accurately, while at the same time rapid development is often mandatory. Recent work has indicated that machine learning techniques in general, and memory-based learning (MBL) in particular, offer the tools to meet both ends. We present examples of modules trained with MBL on three NLP tasks: (i) text-to-speech conversion, (ii) part-of-speech tagging, and (iii) phrase chunking. We demonstrate that the three modules display high generalization accuracy, and argue why MBL is applicable similarly well to a large class of other NLP tasks.

Connected through hyperedge HE-1 (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"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"

Connected through hyperedge HE-2 (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"
    - "GENERALIZATION PERFORMANCE OF BACKPROPAGATION LEARNING ON A SYLLABIFICATION TASK"
    - "Memory-Based Lexical Acquisition and Processing"
    - "Automatic Phonetic Transcription of Words Based On Sparse Data"

Connected through hyperedge HE-3 (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"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"

Connected through hyperedge HE-4 (induced by source paper "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"):
  with members:
    - "Protein Secondary Structure Modelling with Probabilistic Networks (Extended Abstract)"
    - "Towards a Better Understanding of Memory-Based Reasoning Systems"
    - "Dynamical Selection of Learning Algorithms"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Error-Based and Entropy-Based Discretization of Continuous Features"
    - "The Utility of Feature Weighting in Nearest-Neighbor Algorithms"
    - "Fast NP Chunking Using Memory-Based Learning Techniques"
    - "Resolving PP attachment Ambiguities with Memory-Based Learning"

Connected through hyperedge HE-5 (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"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"
    - "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