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: "circulant matrices and differential privacy"
  Abstract: This paper resolves an open problem raised by Blocki {\it et al.} (FOCS 2012), i.e., whether other variants of the Johnson-Lindenstrauss transform preserves differential privacy or not? We prove that a general class of random projection matrices that satisfies the Johnson-Lindenstrauss lemma also preserves differential privacy. This class of random projection matrices requires only $n$ Gaussian samples and $n$ Bernoulli trials and allows matrix-vector multiplication in $O(n \log n)$ time. In this respect, this work unconditionally improves the run time of Blocki {\it et al.} (FOCS 2012) without using the graph sparsification trick of Upadhyay (ASIACRYPT 2013). For the metric of measuring randomness, we stick to the norm used by earlier researchers who studied variants of the Johnson-Lindenstrauss transform and its applications, i.e., count the number of random samples made. In concise, we improve the sampling complexity by quadratic factor, and the run time of cut queries by an $O(n^{o(1)})$ factor and that of covariance queries by an $O(n^{0.38})$ factor.

Connected through hyperedge HE-1 (induced by source paper "mvg mechanism differential privacy under matrix valued query"):
  with members:
    - "a differential privacy mechanism design under matrix valued query"
    - "towards practical differential privacy for sql queries"
    - "coupling random orthonormal projection with gaussian generative model for non interactive private data release"
    - "convex optimization for linear query processing under approximate differential privacy"
    - "understanding the sparse vector technique for differential privacy"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "optimizing batch linear queries under exact and approximate differential privacy"
    - "an improved private mechanism for small databases"

Connected through hyperedge HE-2 (induced by source paper "a differential privacy mechanism design under matrix valued query"):
  with members:
    - "towards practical differential privacy for sql queries"
    - "understanding the sparse vector technique for differential privacy"
    - "privacy via the johnson lindenstrauss transform"
    - "dpcube differentially private histogram release through multidimensional partitioning"
    - "fast private data release algorithms for sparse queries"

Connected through hyperedge HE-3 (induced by source paper "ron gauss enhancing utility in non interactive private data release"):
  with members:
    - "plausible deniability for privacy preserving data synthesis"
    - "differential privacy as a mutual information constraint"
    - "membership inference attacks against machine learning models"
    - "privacy protection for natural language records neural generative models for releasing synthetic twitter data"
    - "unsupervised deep embedding for clustering analysis"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "protecting locations with differential privacy under temporal correlations"

Connected through hyperedge HE-4 (induced by source paper "private incremental regression"):
  with members:
    - "private matchings and allocations"
    - "private approximations of the 2nd moment matrix using existing techniques in linear regression"
    - "sketching as a tool for numerical linear algebra"
    - "randomness efficient fast johnson lindenstrauss transform with applications in differential privacy and compressed sensing"
    - "private empirical risk minimization beyond the worst case the effect of the constraint set geometry"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "private multiplicative weights beyond linear queries"
    - "privacy via the johnson lindenstrauss transform"

Connected through hyperedge HE-5 (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"
    - "differentially private linear algebra in the streaming model"
    - "dimensionality reduction for k means clustering and low rank approximation"

Connected through hyperedge HE-6 (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"
    - "differentially private linear algebra in the streaming model"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "on sketching quadratic forms"):
  with members:
    - "single pass spectral sparsification in dynamic streams"
    - "distributed computation of large scale graph problems"
    - "when distributed computation is communication expensive"
    - "differentially private data analysis of social networks via restricted sensitivity"
    - "iterative constructions and private data release"
    - "fast approximation algorithms for cut based problems in undirected graphs"
    - "twice ramanujan sparsifiers"
    - "spectral sparsification of graphs"

Connected through hyperedge HE-8 (induced by source paper "differentially private linear algebra in the streaming model"):
  with members:
    - "privacy via the johnson lindenstrauss transform"
    - "the noisy power method a meta algorithm with applications"
    - "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"
    - "exploring compact reinforcement learning representations with linear regression"
    - "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)