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 "Adaptive Boosting of Neural Networks for Character Recognition"):
  Abstract: Technical Report #1072, D epartement d'Informatique et Recherche Op erationnelle, Universit e de Montr eal Abstract Boosting is a general method for improving the performance of any learning algorithm that consistently generates classifiers which need to perform only slightly better than random guessing. A recently proposed and very promising boosting algorithm is AdaBoost [5]. It has been applied with great success to several benchmark machine learning problems using rather simple learning algorithms [4], in particular decision trees [1, 2, 6]. In this paper we use AdaBoost to improve the performances of neural networks applied to character recognition tasks. We compare training methods based on sampling the training set and weighting the cost function. Our system achieves about 1.4% error on a data base of online handwritten digits from more than 200 writers. Adaptive boosting of a multi-layer network achieved 2% error on the UCI Letters offline characters data set.

Includes members:
  Member "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree")
  Member "Constraint Tangent Distance for On-line Character Recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Recognizing Handwritten Digits Using Mixtures of Linear Models")
  Member "Experiments with a New Boosting Algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Achieving High-Accuracy Text-to-Speech with Machine Learning")

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