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: "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.

Connected through hyperedge HE-1 (induced by source paper "Constraint Tangent Distance for On-line Character Recognition"):
  with members:
    - "Recognizing Handwritten Digits Using Mixtures of Linear Models"

Connected through hyperedge HE-2 (induced by source paper "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"):
  with members:
    - "Learning Sparse Perceptrons"
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "An Efficient Boosting Algorithm for Combining Preferences"
    - "BOOSTING AND NAIVE BAYESIAN LEARNING"
    - "Actively Searching for an Effective Neural-Network Ensemble"
    - "Learning to Order Things"
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "Context-sensitive learning methods for text categorization"

Connected through hyperedge HE-3 (induced by source paper "Experiments with a New Boosting Algorithm"):
  with members:
    - "Improving Bagging Performance by Increasing Decision Tree Diversity"
    - "Boosting Trees for Cost-Sensitive Classifications"
    - "A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets"
    - "Pruning Adaptive Boosting ICML-97 Final Draft"
    - "Submitted to the Future Generation Computer Systems special issue on Data Mining. Using Neural Networks"
    - "Achieving High-Accuracy Text-to-Speech with Machine Learning"
    - "An Empirical Evaluation of Bagging and Boosting"
    - "Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a"

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