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: "Fast NP Chunking Using Memory-Based Learning Techniques"
  Abstract: In this paper we discuss the application of Memory-Based Learning (MBL) to fast NP chunking. We first discuss the application of a fast decision tree variant of MBL (IGTree) on the dataset described in (Ramshaw and Marcus, 1995), which consists of roughly 50,000 test and 200,000 train items. In a second series of experiments we used an architecture of two cascaded IGTrees. In the second level of this cascaded classifier we added context predictions as extra features so that incorrect predictions from the first level can be corrected, yielding a 97.2% generalisation accuracy with training and testing times in the order of seconds to minutes.

Connected through hyperedge HE-1 (induced by source paper "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING"):
  with members:
    - "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"
    - "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"
    - "GENERALIZATION PERFORMANCE OF BACKPROPAGATION LEARNING ON A SYLLABIFICATION TASK"
    - "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-3 (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"
    - "Resolving PP attachment Ambiguities with Memory-Based Learning"
    - "Memory-Based Lexical Acquisition and Processing"

Connected through hyperedge HE-4 (induced by source paper "GENERALIZATION PERFORMANCE OF BACKPROPAGATION LEARNING ON A SYLLABIFICATION TASK"):
  with members:
    - "Resolving PP attachment Ambiguities with Memory-Based Learning"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "Oblivious Decision Trees and Abstract Cases"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "Using Decision Trees to Improve Case-Based Learning"
    - "Induction of Condensed Determinations"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"

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