human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "smooth sensitivity based approach for differentially private principal component analysis"):
  Abstract: Currently known methods for this task either employ the computationally intensive \emph{exponential mechanism} or require an access to the covariance matrix, and therefore fail to utilize potential sparsity of the data. The problem of designing simpler and more efficient methods for this task has been raised as an open problem in \cite{kapralov2013differentially}. #R##N#In this paper we address this problem by employing the output perturbation mechanism. Despite being arguably the simplest and most straightforward technique, it has been overlooked due to the large \emph{global sensitivity} associated with publishing the leading eigenvector. We tackle this issue by adopting a \emph{smooth sensitivity} based approach, which allows us to establish differential privacy (in a worst-case manner) and near-optimal sample complexity results under eigengap assumption. We consider both the pure and the approximate notions of differential privacy, and demonstrate a tradeoff between privacy level and sample complexity. We conclude by suggesting how our results can be extended to related problems.

Includes members:
  Member "no spurious local minima in nonconvex low rank problems a unified geometric analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "no spurious local minima in deep quadratic networks")
  Member "fast rates for empirical risk minimization of strict saddle problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding generalization in deep learning via tensor methods")
  Member "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "efficient frequent directions algorithm for sparse matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deterministic streaming sketch for ridge regression")
  Member "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "faster eigenvector computation via shift and invert preconditioning")
  Member "convergence of stochastic gradient descent for pca":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exponentially convergent stochastic k pca without variance reduction")
  Member "low rank approximation and regression in input sparsity time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "beating randomized response on incoherent matrices":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mvg mechanism differential privacy under matrix valued query")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.LG(Machine Learning)