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 "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"):
  Abstract: The performance of the error backpropagation (BP) and ID3 learning algorithms was compared on the task of mapping English text to phonemes and stresses. Under the distributed output code developed by Sejnowski and Rosenberg, it is shown that BP consistently out-performs ID3 on this task by several percentage points. Three hypotheses explaining this difference were explored: (a) ID3 is overfitting the training data, (b) BP is able to share hidden units across several output units and hence can learn the output units better, and (c) BP captures statistical information that ID3 does not. We conclude that only hypothesis (c) is correct. By augmenting ID3 with a simple statistical learning procedure, the performance of BP can be approached but not matched. More complex statistical procedures can improve the performance of both BP and ID3 substantially. A study of the residual errors suggests that there is still substantial room for improvement in learning methods for text-to-speech mapping.

Includes members:
  Member "Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Back Propagation is Sensitive to Initial Conditions")
  Member "Statistical Tests for Comparing Supervised Classification Learning Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Back Propagation is Sensitive to Initial Conditions")
  Member "Constructing Nominal Xof-N Attributes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Effects of Different Types of New Attribute on Constructive Induction")
  Member "Improving accuracy by combining rule-based and case-based reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A comparison of Anapron with seven other name-pronunciation systems")
  Member "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Symbolic and Subsymbolic Learning for Vision: Some Possibilities")
  Member "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "On Pruning and Averaging Decision Trees")
  Member "Draft Symbolic Representation of Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference")
  Member "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "Inverse entailment and Progol")

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