human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Statistical Queries and Faulty PAC Oracles"):
  Abstract: In this paper we study learning in the PAC model of Valiant [18] in which the example oracle used for learning may be faulty in one of two ways: either by misclassifying the example or by distorting the distribution of examples. We first consider models in which examples are misclassified. Kearns [12] recently showed that efficient learning in a new model using statistical queries is a sufficient condition for PAC learning with classification noise. We show that efficient learning with statistical queries is sufficient for learning in the PAC model with malicious error rate proportional to the required statistical query accuracy. One application of this result is a new lower bound for tolerable malicious error in learning monomials of k literals. This is the first such bound which is independent of the number of irrelevant attributes n. We also use the statistical query model to give sufficient conditions for using distribution specific algorithms on distributions outside their prescribed domains. A corollary of this result expands the class of distributions on which we can weakly learn monotone Boolean formulae. We also consider new models of learning in which examples are not chosen according to the distribution on which the learner will be tested. We examine three variations of distribution noise and give necessary and sufficient conditions for polynomial time learning with such noise. We show containments and separations between the various models of faulty oracles. Finally, we examine hypothesis boosting algorithms in the context of learning with distribution noise, and show that Schapire's result regarding the strength of weak learnabil-ity [17] is in some sense tight in requiring the weak learner to be nearly distribution free.

Includes members:
  Member "25 Learning in Hybrid Noise Environments Using Statistical Queries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Improved Noise-Tolerant Learning and Generalized Statistical Queries")
  Member "Learning in the Presence of Malicious Errors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning")
  Member "On Learning from Noisy and Incomplete Examples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Improved Noise-Tolerant Learning and Generalized Statistical Queries")
  Member "On Learning Visual Concepts and DNF Formulae":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Learning in the Presence of Malicious Errors")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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