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: "An Empirical Evaluation of Bagging and Boosting"
  Abstract: An ensemble consists of a set of independently trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman 1996a) and Boosting (Freund & Schapire 1996) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods using both neural networks and decision trees as our classification algorithms. Our results clearly show two important facts. The first is that even though Bagging almost always produces a better classifier than any of its individual component classifiers and is relatively impervious to overfitting, it does not generalize any better than a baseline neural-network ensemble method. The second is that Boosting is a powerful technique that can usually produce better ensembles than Bagging; however, it is more susceptible to noise and can quickly overfit a data set.

Connected through hyperedge HE-1 (induced by source paper "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"):
  with members:
    - "Pruning Recurrent Neural Networks for Improved Generalization Performance"
    - "Actively Searching for an Effective Neural-Network Ensemble"
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"

Connected through hyperedge HE-2 (induced by source paper "Actively Searching for an Effective Neural-Network Ensemble"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"
    - "Learning from Bad Data"

Connected through hyperedge HE-3 (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"
    - "Experiments with a New Boosting Algorithm"
    - "An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants"

Connected through hyperedge HE-4 (induced by source paper "Generating Accurate and Diverse Members of a Neural-Network Ensemble"):
  with members:
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"
    - "A New Metric-Based Approach to Model Selection"
    - "Using Neural Networks to Automatically Refine Expert System Knowledge Bases: Experiments in the NYNEX MAX Domain"

Connected through hyperedge HE-5 (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"
    - "Adaptive Boosting of Neural Networks for Character Recognition"
    - "Submitted to the Future Generation Computer Systems special issue on Data Mining. Using Neural Networks"
    - "Achieving High-Accuracy Text-to-Speech with Machine Learning"
    - "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: Theory