human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "on Inductive Logic Programming (ILP-95) Inducing Logic Programs without Explicit Negative Examples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "The Difficulties of Learning Logic Programs with Cut")
  Member "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Effects of Different Types of New Attribute on Constructive Induction")
  Member "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms")
  Member "Learning First-Order Definitions of Functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Combining FOIL and EBG to Speed-up Logic Programs")
  Member "Memory-Based Lexical Acquisition and Processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING")
  Member "Inverse entailment and Progol":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Predicting Ordinal Classes in ILP")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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