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: "Improving accuracy by combining rule-based and case-based reasoning"
  Abstract: An architecture is presented for combining rule-based and case-based reasoning. The architecture is intended for domains that are understood reasonably well, but still imperfectly. It uses a set of rules, which are taken to be only approximately correct, to obtain a preliminary answer for a given problem; it then draws analogies from cases to handle exceptions to the rules. Having rules together with cases not only increases the architecture's domain coverage, it also allows innovative ways of doing case-based reasoning: the same rules that are used for rule-based reasoning are also used by the case-based component to do case indexing and case adaptation. The architecture was applied to the task of name pronunciation, and, with minimal knowledge engineering, was found to perform almost at the level of the best commercial systems. Moreover, its accuracy was found to exceed what it could have achieved with rules or cases alone, thus demonstrating the accuracy improvement afforded by combining rule-based and case-based reasoning. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories of Cambridge, Massachusetts; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories. All rights reserved.

Connected through hyperedge HE-1 (induced by source paper "A comparison of Anapron with seven other name-pronunciation systems"):
  with members:
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "The evaluation of Anapron: A case study in evaluating a case-based system"

Connected through hyperedge HE-2 (induced by source paper "The evaluation of Anapron: A case study in evaluating a case-based system"):
  with members:
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "A comparison of Anapron with seven other name-pronunciation systems"

Connected through hyperedge HE-3 (induced by source paper "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"):
  with members:
    - "Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and"
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Constructing Nominal Xof-N Attributes"
    - "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Draft Symbolic Representation of Neural Networks"
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"
    - "Generalizing from Case Studies: A Case Study"

Connected through hyperedge HE-4 (induced by source paper "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"):
  with members:
    - "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"
    - "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING"
    - "Automatic Phonetic Transcription of Words Based On Sparse Data"

Connected through hyperedge HE-5 (induced by source paper "Case-based Acquisition of User Preferences for Solution Improvement in Ill-Structured Domains"):
  with members:
    - "Using Case-Based Reasoning as a Reinforcement Learning Framework for Optimization with Changing Criteria"
    - "Using Case-Based Reasoning to Acquire User Scheduling Preferences that Change over Time"
    - "Modeling Ill-Structured Optimization Tasks through Cases"

Connected through hyperedge HE-6 (induced by source paper "Theory Refinement Combining Analytical and Empirical Methods"):
  with members:
    - "An Optimized Theory Revision Module"
    - "The Challenge of Revising an Impure Theory"
    - "Learning Analytically and Inductively"
    - "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning"
    - "From Theory Refinement to KB Maintenance: a Position Statement"
    - "Tractability of Theory Patching"
    - "Revising Bayesian Network Parameters Using Backpropagation"
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"

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