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: "private approximations of the 2nd moment matrix using existing techniques in linear regression"
  Abstract: We introduce three differentially-private algorithms that approximates the 2nd-moment matrix of the data. These algorithm, which in contrast to existing algorithms output positive-definite matrices, correspond to existing techniques in linear regression literature. Specifically, we discuss the following three techniques. (i) For Ridge Regression, we propose setting the regularization coefficient so that by approximating the solution using Johnson-Lindenstrauss transform we preserve privacy. (ii) We show that adding a small batch of random samples to our data preserves differential privacy. (iii) We show that sampling the 2nd-moment matrix from a Bayesian posterior inverse-Wishart distribution is differentially private provided the prior is set correctly. We also evaluate our techniques experimentally and compare them to the existing "Analyze Gauss" algorithm of Dwork et al.

Connected through hyperedge HE-1 (induced by source paper "utility preserving secure private data release"):
  with members:
    - "membership inference attacks against machine learning models"
    - "differentially private ordinary least squares t values confidence intervals and rejecting null hypotheses"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "privacy via the johnson lindenstrauss transform"
    - "the johnson lindenstrauss transform itself preserves differential privacy"

Connected through hyperedge HE-2 (induced by source paper "differentially private contextual linear bandits"):
  with members:
    - "achieving privacy in the adversarial multi armed bandit"
    - "finite sample differentially private confidence intervals"

Connected through hyperedge HE-3 (induced by source paper "private incremental regression"):
  with members:
    - "private matchings and allocations"
    - "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"
    - "circulant matrices and differential privacy"
    - "private multiplicative weights beyond linear queries"
    - "privacy via the johnson lindenstrauss transform"

Connected through hyperedge HE-4 (induced by source paper "a note on privacy preserving iteratively reweighted least squares"):
  with members:
    - "the noisy power method a meta algorithm with applications"

Connected through hyperedge HE-5 (induced by source paper "differentially private ordinary least squares t values confidence intervals and rejecting null hypotheses"):
  with members:
    - "differentially private hypothesis testing revisited"
    - "randomized sketches of convex programs with sharp guarantees"
    - "differentially private linear algebra in the streaming model"
    - "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"

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)