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: "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.

Connected through hyperedge HE-1 (induced by source paper "Effects of Different Types of New Attribute on Constructive Induction"):
  with members:
    - "Constructing Nominal Xof-N Attributes"
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "Continuous-valued Xof-N Attributes Versus Nominal Xof-N Attributes for Constructive Induction: A Case Study"

Connected through hyperedge HE-2 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"):
  with members:
    - "Pessimistic decision tree pruning based on tree size"
    - "Multivariate Decision Trees"
    - "Dynamic Automatic Model Selection"
    - "Finding new rules for incomplete theories: Explicit biases for induction with contextual information. In Proceedings"
    - "Induction of Oblique Decision Trees"
    - "Data Analyses Using Simulated Breeding and Inductive Learning Methods"
    - "Learning Decision Trees from Decision Rules:"
    - "The Estimation of Probabilities in Attribute Selection Measures for Decision Tree Induction"

Connected through hyperedge HE-3 (induced by source paper "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"):
  with members:
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"
    - "Symbolic and Subsymbolic Learning for Vision: Some Possibilities"
    - "Forward models: Supervised learning with a distal teacher"
    - "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning"
    - "REINFORCEMENT LEARNING FOR COORDINATED REACTIVE CONTROL"
    - "Refining PID Controllers using Neural Networks"

Connected through hyperedge HE-4 (induced by source paper "Achieving High-Accuracy Text-to-Speech with Machine Learning"):
  with members:
    - "Experiments with a New Boosting Algorithm"

Connected through hyperedge HE-5 (induced by source paper "A comparison of Anapron with seven other name-pronunciation systems"):
  with members:
    - "Improving accuracy by combining rule-based and case-based reasoning"
    - "The evaluation of Anapron: A case study in evaluating a case-based system"

Connected through hyperedge HE-6 (induced by source paper "The evaluation of Anapron: A case study in evaluating a case-based system"):
  with members:
    - "Improving accuracy by combining rule-based and case-based reasoning"
    - "A comparison of Anapron with seven other name-pronunciation systems"

Connected through hyperedge HE-7 (induced by source paper "Continuous-valued Xof-N Attributes Versus Nominal Xof-N Attributes for Constructive Induction: A Case Study"):
  with members:
    - "Constructing Nominal Xof-N Attributes"
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "CONSTRUCTING CONJUNCTIVE TESTS FOR DECISION TREES"
    - "Effects of Different Types of New Attribute on Constructive Induction"

Connected through hyperedge HE-8 (induced by source paper "Improving accuracy by combining rule-based and case-based reasoning"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"
    - "Case-based Acquisition of User Preferences for Solution Improvement in Ill-Structured Domains"
    - "The evaluation of Anapron: A case study in evaluating a case-based system"
    - "A comparison of Anapron with seven other name-pronunciation systems"

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