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: "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
  Abstract: We introduce two boosting algorithms that aim to increase the generalization accuracy of a given classifier by incorporating it as a level-0 component in a stacked generalizer. Both algorithms construct a complementary level-0 classifier that can only generate coarse hypotheses for the training data. We show that the two algorithms boost generalization accuracy on a representative collection of data sets. The two algorithms are distinguished in that one of them modifies the class targets of selected training instances in order to train the complementary classifier. We show that the two algorithms achieve approximately equal generalization accuracy, but that they create complementary classifiers that display different degrees of accuracy and diversity. Our study provides evidence that it may be useful to investigate families of boosting algorithms that incorporate varying levels of accuracy and diversity, so as to achieve an appropriate mix for a given task and domain.

Connected through hyperedge HE-1 (induced by source paper "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"
    - "Growing Simpler Decision Trees to Facilitate Knowledge Discovery"
    - "Automated model selection"
    - "Oblivious Decision Trees and Abstract Cases"
    - "Generalizing from Case Studies: A Case Study"
    - "Compression-Based Feature Subset Selection  Keywords: Minimum Description Length Principle, Cross Validation, Noise"
    - "On the Greediness of Feature Selection Algorithms"

Connected through hyperedge HE-2 (induced by source paper "Error Reduction through Learning Multiple Descriptions"):
  with members:
    - "Stochastically Guided Disjunctive Version Space Learning"

Connected through hyperedge HE-3 (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"
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"
    - "An Empirical Evaluation of Bagging and Boosting"

Connected through hyperedge HE-4 (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"
    - "Context-sensitive learning methods for text categorization"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"

Connected through hyperedge HE-5 (induced by source paper "Generating Accurate and Diverse Members of a Neural-Network Ensemble"):
  with members:
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"
    - "An Empirical Evaluation of Bagging and Boosting"
    - "A New Metric-Based Approach to Model Selection"
    - "Using Neural Networks to Automatically Refine Expert System Knowledge Bases: Experiments in the NYNEX MAX Domain"

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