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: "On the Induction of Intelligible Ensembles"
  Abstract: Ensembles of classifiers, e.g. decision trees, often exhibit greater predictive accuracy than single classifiers alone. Bagging and boosting are two standard ways of generating and combining multiple classifiers. Unfortunately, the increase in predictive performance is usually linked to a dramatic decrease in intelligibility: ensembles are more or less black boxes comparable to neural networks. So far attempts at pruning of ensembles have not been very successful, approximately reducing ensembles into half. This paper describes a different approach which both tries to keep ensemble-sizes small during induction already and also limits the complexity of single classifiers rigorously. Single classifiers are decision-stumps of a prespecified maximal depth. They are combined by majority voting. Ensembles are induced and pruned by a simple hill-climbing procedure. These ensembles can reasonably be transformed into equivalent decision trees. We conduct some empirical evaluation to investigate both predictive accuracies and classifier complexities.

Connected through hyperedge HE-1 (induced by source paper "On Pruning and Averaging Decision Trees"):
  with members:
    - "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree"

Connected through hyperedge HE-2 (induced by source paper "Oblivious Decision Trees, Graphs, and Top-Down Pruning"):
  with members:
    - "Targeting Business Users with Decision Table Classifiers"

Connected through hyperedge HE-3 (induced by source paper "Estimating the Accuracy of Learned Concepts"):
  with members:
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"

Connected through hyperedge HE-4 (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"
    - "An Empirical Evaluation of Bagging and Boosting"

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