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: "Generalizations of the Bias/Variance Decomposition for Prediction Error"
  Abstract: The bias and variance of a real valued random variable, using squared error loss, are well understood. However because of recent developments in classification techniques it has become desirable to extend these concepts to general random variables and loss functions. The 0-1 (misclassification) loss function with categorical random variables has been of particular interest. We explore the concepts of variance and bias and develop a decomposition of the prediction error into functions of the systematic and variable parts of our predictor. After providing some examples we conclude with a discussion of the various definitions that have been proposed.

Connected through hyperedge HE-1 (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