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: "Bias and the Quantification of Stability Bias and the Quantification of Stability Bias and the"
  Abstract: Research on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy. We believe that there are other factors that should also play a role in the evaluation of bias. One such factor is the stability of the algorithm; in other words, the repeatability of the results. If we obtain two sets of data from the same phenomenon, with the same underlying probability distribution, then we would like our learning algorithm to induce approximately the same concepts from both sets of data. This paper introduces a method for quantifying stability, based on a measure of the agreement between concepts. We also discuss the relationships among stability, predictive accuracy, and bias.

Connected through hyperedge HE-1 (induced by source paper "Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction"):
  with members:
    - "Learning Decision Lists Using Homogeneous Rules"
    - "Lookahead and Pathology in Decision Tree Induction"
    - "In Defense of C4.5: Notes on Learning One-Level Decision Trees"
    - "What should be minimized in a decision tree: A re-examination"

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