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: "Experiments with a New Boosting Algorithm"
  Abstract: In an earlier paper, we introduced a new boosting algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the related notion of a pseudo-loss which is a method for forcing a learning algorithm of multi-label concepts to concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems. We performed two sets of experiments. The first set compared boosting to Breiman's bagging method when used to aggregate various classifiers (including decision trees and single attribute-value tests). We compared the performance of the two methods on a collection of machine-learning benchmarks. In the second set of experiments, we studied in more detail the performance of boosting using a nearest-neighbor classifier on an OCR problem.

Connected through hyperedge HE-1 (induced by source paper "Achieving High-Accuracy Text-to-Speech with Machine Learning"):
  with members:
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"

Connected through hyperedge HE-2 (induced by source paper "On the Induction of Intelligible Ensembles"):
  with members:
    - "On Pruning and Averaging Decision Trees"
    - "Estimating the Accuracy of Learned Concepts"
    - "Oblivious Decision Trees, Graphs, and Top-Down Pruning"

Connected through hyperedge HE-3 (induced by source paper "An Empirical Evaluation of Bagging and Boosting"):
  with members:
    - "Improving Bagging Performance by Increasing Decision Tree Diversity"
    - "Actively Searching for an Effective Neural-Network Ensemble"
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"

Connected through hyperedge HE-4 (induced by source paper "Pruning Adaptive Boosting ICML-97 Final Draft"):
  with members:
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"

Connected through hyperedge HE-5 (induced by source paper "Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a"):
  with members:
    - "Bias Plus Variance Decomposition for Zero-One Loss Functions"
    - "Bayesian Model Averaging"

Connected through hyperedge HE-6 (induced by source paper "Boosting Trees for Cost-Sensitive Classifications"):
  with members:
    - "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"
    - "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods"

Connected through hyperedge HE-7 (induced by source paper "Improving Bagging Performance by Increasing Decision Tree Diversity"):
  with members:
    - "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods"
    - "An Empirical Evaluation of Bagging and Boosting"
    - "An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants"

Connected through hyperedge HE-8 (induced by source paper "Adaptive Boosting of Neural Networks for Character Recognition"):
  with members:
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Constraint Tangent Distance for On-line Character Recognition"

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: Theory