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: "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"
  Abstract: Learning the past tense of English verbs | a seemingly minor aspect of language acquisition | has generated heated debates since 1986, and has become a landmark task for testing the adequacy of cognitive modeling. Several artificial neural networks (ANNs) have been implemented, and a challenge for better symbolic models has been posed. In this paper, we present a general-purpose Symbolic Pattern Associator (SPA) based upon the decision-tree learning algorithm ID3. We conduct extensive head-to-head comparisons on the generalization ability between ANN models and the SPA under different representations. We conclude that the SPA generalizes the past tense of unseen verbs better than ANN models by a wide margin, and we offer insights as to why this should be the case. We also discuss a new default strategy for decision-tree learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "Inverse entailment and Progol"):
  with members:
    - "Predicting Ordinal Classes in ILP"
    - "Learning Trees and Rules with Set-valued Features"
    - "Stochastic Propositionalization of Non-Determinate Background Knowledge"
    - "Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail"
    - "Extraction of Meta-Knowledge to Restrict the Hypothesis Space for ILP Systems incorporating them in FOIL."
    - "Which Hypotheses Can Be Found with Inverse Entailment? -Extended Abstract"
    - "Bottom-up induction of logic programs with more than one recursive clause"
    - "Relational Knowledge Discovery in Databases"

Connected through hyperedge HE-2 (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"
    - "Improving accuracy by combining rule-based and case-based reasoning"
    - "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"
    - "Generalizing from Case Studies: A Case Study"

Connected through hyperedge HE-3 (induced by source paper "Memory-Based Lexical Acquisition and Processing"):
  with members:
    - "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"
    - "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"

Connected through hyperedge HE-4 (induced by source paper "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"):
  with members:
    - "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"
    - "Error-Correcting Output Coding Corrects Bias and Variance"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"
    - "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"
    - "Error-Correcting Output Codes for Local Learners"
    - "Probability Estimation via Error-Correcting Output Coding"

Connected through hyperedge HE-5 (induced by source paper "on Inductive Logic Programming (ILP-95) Inducing Logic Programs without Explicit Negative Examples"):
  with members:
    - "Learning Semantic Grammars with Constructive Inductive Logic Programming"
    - "The Difficulties of Learning Logic Programs with Cut"

Connected through hyperedge HE-6 (induced by source paper "Learning First-Order Definitions of Functions"):
  with members:
    - "Combining FOIL and EBG to Speed-up Logic Programs"
    - "An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions"

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