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 "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.

Includes members:
  Member "Error Reduction through Learning Multiple Descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Stochastically Guided Disjunctive Version Space Learning")
  Member "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "On the Greediness of Feature Selection Algorithms")
  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-3 (induced by source paper "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree")
  Member "Generating Accurate and Diverse Members of a Neural-Network Ensemble":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "An Empirical Evaluation of Bagging and Boosting")
  Member "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "An Empirical Evaluation of Bagging and Boosting")

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: Theory