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: "Resolving PP attachment Ambiguities with Memory-Based Learning"
  Abstract: In this paper we describe the application of Memory-Based Learning to the problem of Prepositional Phrase attachment disambiguation. We compare Memory-Based Learning, which stores examples in memory and generalizes by using intelligent similarity metrics, with a number of recently proposed statistical methods that are well suited to large numbers of features. We evaluate our methods on a common benchmark dataset and show that our method compares favorably to previous methods, and is well-suited to incorporating various unconventional representations of word patterns such as value difference metrics and Lexical Space.

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

Connected through hyperedge HE-2 (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"
    - "Memory-Based Lexical Acquisition and Processing"

Connected through hyperedge HE-3 (induced by source paper "GENERALIZATION PERFORMANCE OF BACKPROPAGATION LEARNING ON A SYLLABIFICATION TASK"):
  with members:
    - "Fast NP Chunking Using Memory-Based Learning Techniques"
    - "Memory-Based Lexical Acquisition and Processing"
    - "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING"
    - "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