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: "Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a"
  Abstract: The error rate of decision-tree and other classification learners can often be much reduced by bagging: learning multiple models from bootstrap samples of the database, and combining them by uniform voting. In this paper we empirically test two alternative explanations for this, both based on Bayesian learning theory: (1) bagging works because it is an approximation to the optimal procedure of Bayesian model averaging, with an appropriate implicit prior; (2) bagging works because it effectively shifts the prior to a more appropriate region of model space. All the experimental evidence contradicts the first hypothesis, and confirms the second. Bagging (Breiman 1996a) is a simple and effective way to reduce the error rate of many classification learning algorithms. For example, in the empirical study described below, it reduces the error of a decision-tree learner in 19 of 26 databases, by 4% on average. In the bagging procedure, given a training set of size s, a "bootstrap" replicate of it is constructed by taking s samples with replacement from the training set. Thus a new training set of the same size is produced, where each of the original examples may appear once, more than once, or not. On average, 63% of the original examples will appear in the bootstrap sample. The learning algorithm is then applied to this training set. This procedure is repeated m times, and the resulting m models are aggregated by uniform voting. Bagging is one of several "multiple model" approaches that have recently received much attention (see, for example, (Chan, Stolfo, & Wolpert 1996)). Other procedures of this type include boosting (Freund & Schapire 1996) and stacking (Wolpert 1992).

Connected through hyperedge HE-1 (induced by source paper "Bias Plus Variance Decomposition for Zero-One Loss Functions"):
  with members:
    - "On learning hierarchical classifications"
    - "The Utility of Feature Weighting in Nearest-Neighbor Algorithms"
    - "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms"
    - "Characterizing the generalization performance of model selection strategies"
    - "Error-Correcting Output Codes for Local Learners"
    - "Bias, variance and prediction error for classification rules"

Connected through hyperedge HE-2 (induced by source paper "Bayesian Model Averaging"):
  with members:
    - "An Improved Model for Spatially Correlated Binary Responses"

Connected through hyperedge HE-3 (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