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: "Exploiting the Omission of Irrelevant Data"
  Abstract: Most learning algorithms work most effectively when their training data contain completely specified labeled samples. In many diagnostic tasks, however, the data will include the values of only some of the attributes; we model this as a blocking process that hides the values of those attributes from the learner. While blockers that remove the values of critical attributes can handicap a learner, this paper instead focuses on blockers that remove only irrelevant attribute values, i.e., values that are not needed to classify an instance, given the values of the other unblocked attributes. We first motivate and formalize this model of "superfluous-value blocking", and then demonstrate that these omissions can be useful, by proving that certain classes that seem hard to learn in the general PAC model | viz., decision trees and DNF formulae | are trivial to learn in this setting. We also show that this model can be extended to deal with (1) theory revision (i.e., modifying an existing formula); (2) blockers that occasionally include superfluous values or exclude required values; and (3) other cor ruptions of the training data.

Connected through hyperedge HE-1 (induced by source paper "Learning Active Classifiers"):
  with members:
    - "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"
    - "Learning Default Concepts"
    - "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"
    - "Learning to Reason with a Restricted View"

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