human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "differentially private linear algebra in the streaming model"
  Abstract: Numerical linear algebra plays an important role in computer science. In this paper, we initiate the study of performing linear algebraic tasks while preserving privacy when the data is streamed online. Our main focus is the space requirement of the privacy-preserving data-structures. We give the first {\em sketch-based} algorithm for differential privacy. We give optimal, up to logarithmic factor, space data-structures that can compute low rank approximation, linear regression, and matrix multiplication, while preserving differential privacy with better additive error bounds compared to the known results. Notably, we match the best known space bound in the non-private setting by Kane and Nelson (J. ACM, 61(1):4). #R##N#Our mechanism for differentially private low-rank approximation {\em reuses} the random Gaussian matrix in a specific way to provide a single-pass mechanism. We prove that the resulting distribution also preserve differential privacy. This can be of independent interest. We do not make any assumptions, like singular value separation or normalized row assumption, as made in the earlier works. The mechanisms for matrix multiplication and linear regression can be seen as the private analogues of the known non-private algorithms. All our mechanisms, in the form presented, can also be computed in the distributed setting.

Connected through hyperedge HE-1 (induced by source paper "the price of differential privacy for low rank factorization"):
  with members:
    - "communication efficient algorithms for distributed stochastic principal component analysis"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "optimal principal component analysis in distributed and streaming models"
    - "wishart mechanism for differentially private principal components analysis"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "improved distributed principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "fast and space optimal low rank factorization in the streaming model with application in differential privacy"):
  with members:
    - "optimal principal component analysis in distributed and streaming models"
    - "wishart mechanism for differentially private principal components analysis"
    - "randomness efficient fast johnson lindenstrauss transform with applications in differential privacy and compressed sensing"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "the noisy power method a meta algorithm with applications"
    - "low rank approximation and regression in input sparsity time"
    - "calibrating data to sensitivity in private data analysis"
    - "the johnson lindenstrauss transform itself preserves differential privacy"

Connected through hyperedge HE-3 (induced by source paper "on low space differentially private low rank factorization in the spectral norm"):
  with members:
    - "the price of differential privacy for low rank factorization"
    - "optimal principal component analysis in distributed and streaming models"
    - "wishart mechanism for differentially private principal components analysis"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "circulant matrices and differential privacy"
    - "the noisy power method a meta algorithm with applications"
    - "low rank approximation and regression in input sparsity time"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-4 (induced by source paper "differentially private ordinary least squares t values confidence intervals and rejecting null hypotheses"):
  with members:
    - "differentially private hypothesis testing revisited"
    - "private approximations of the 2nd moment matrix using existing techniques in linear regression"
    - "randomized sketches of convex programs with sharp guarantees"
    - "private multiplicative weights beyond linear queries"
    - "calibrating data to sensitivity in private data analysis"
    - "convergence rates for differentially private statistical estimation"
    - "the johnson lindenstrauss transform itself preserves differential privacy"
    - "beating randomized response on incoherent matrices"

Connected through hyperedge HE-5 (induced by source paper "private approximations of the 2nd moment matrix using existing techniques in linear regression"):
  with members:
    - "on the differential privacy of bayesian inference"
    - "private multiplicative weights beyond linear queries"
    - "the johnson lindenstrauss transform itself preserves differential privacy"
    - "beating randomized response on incoherent matrices"
    - "randomized algorithms for matrices and data"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-6 (induced by source paper "circulant matrices and differential privacy"):
  with members:
    - "restricted isometry property for general p norms"
    - "privacy via the johnson lindenstrauss transform"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private data analysis of social networks via restricted sensitivity"
    - "the johnson lindenstrauss transform itself preserves differential privacy"
    - "beating randomized response on incoherent matrices"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "beyond worst case analysis in private singular vector computation"

Question: Please tell me which class the center node 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.DS(Data Structures and Algorithms)