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: "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.

Connected through hyperedge HE-1 (induced by source paper "On Learning from Noisy and Incomplete Examples"):
  with members:
    - "25 Learning in Hybrid Noise Environments Using Statistical Queries"
    - "Improved Noise-Tolerant Learning and Generalized Statistical Queries"
    - "Pac Learning, Noise, and Geometry"

Connected through hyperedge HE-2 (induced by source paper "Learning in the Presence of Malicious Errors"):
  with members:
    - "25 Learning in Hybrid Noise Environments Using Statistical Queries"
    - "On Learning Visual Concepts and DNF Formulae"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Pac Learning, Noise, and Geometry"
    - "Tracking Drifting Concepts By Minimizing Disagreements"
    - "PAC-Learning PROLOG clauses with or without errors"
    - "On Learning Conjunctions with Malicious Noise"
    - "Toward Efficient Agnostic Learning"

Connected through hyperedge HE-3 (induced by source paper "25 Learning in Hybrid Noise Environments Using Statistical Queries"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"
    - "Improved Noise-Tolerant Learning and Generalized Statistical Queries"
    - "On Learning from Noisy and Incomplete Examples"

Connected through hyperedge HE-4 (induced by source paper "On Learning Visual Concepts and DNF Formulae"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"
    - "Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis"
    - "Learning Conjunctions of Horn Clauses"
    - "On Learning Read-k-Satisfy-j DNF"

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