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: "the johnson lindenstrauss transform itself preserves differential privacy"
  Abstract: This paper proves that an "old dog", namely -- the classical Johnson-Lindenstrauss transform, "performs new tricks" -- it gives a novel way of preserving differential privacy. We show that if we take two databases, $D$ and $D'$, such that (i) $D'-D$ is a rank-1 matrix of bounded norm and (ii) all singular values of $D$ and $D'$ are sufficiently large, then multiplying either $D$ or $D'$ with a vector of iid normal Gaussians yields two statistically close distributions in the sense of differential privacy. Furthermore, a small, deterministic and \emph{public} alteration of the input is enough to assert that all singular values of $D$ are large. #R##N#We apply the Johnson-Lindenstrauss transform to the task of approximating cut-queries: the number of edges crossing a $(S,\bar S)$-cut in a graph. We show that the JL transform allows us to \emph{publish a sanitized graph} that preserves edge differential privacy (where two graphs are neighbors if they differ on a single edge) while adding only $O(|S|/\epsilon)$ random noise to any given query (w.h.p). Comparing the additive noise of our algorithm to existing algorithms for answering cut-queries in a differentially private manner, we outperform all others on small cuts ($|S| = o(n)$). #R##N#We also apply our technique to the task of estimating the variance of a given matrix in any given direction. The JL transform allows us to \emph{publish a sanitized covariance matrix} that preserves differential privacy w.r.t bounded changes (each row in the matrix can change by at most a norm-1 vector) while adding random noise of magnitude independent of the size of the matrix (w.h.p). In contrast, existing algorithms introduce an error which depends on the matrix dimensions.

Connected through hyperedge HE-1 (induced by source paper "crypt epsilon crypto assisted differential privacy on untrusted servers"):
  with members:
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "mvg mechanism differential privacy under matrix valued query"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "ektelo a framework for defining differentially private computations"
    - "shrinkwrap differentially private query processing in private data federations"
    - "locally differentially private heavy hitter identification"
    - "renyi differential privacy"
    - "prochlo strong privacy for analytics in the crowd"

Connected through hyperedge HE-2 (induced by source paper "outis crypto assisted differential privacy on untrusted servers"):
  with members:
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "mvg mechanism differential privacy under matrix valued query"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "ektelo a framework for defining differentially private computations"
    - "shrinkwrap differentially private query processing in private data federations"
    - "locally differentially private heavy hitter identification"
    - "marginal release under local differential privacy"
    - "cryptodl deep neural networks over encrypted data"

Connected through hyperedge HE-3 (induced by source paper "interpretable and differentially private predictions"):
  with members:
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "smoothgrad removing noise by adding noise"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "machine learning models that remember too much"
    - "preserving differential privacy in convolutional deep belief networks"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-4 (induced by source paper "matrix sketching for secure collaborative machine learning"):
  with members:
    - "gradient descent with compressed iterates"
    - "exploiting unintended feature leakage in collaborative learning"
    - "differentially private data generative models"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "fast approximation of matrix coherence and statistical leverage"
    - "randomized algorithms for matrices and data"
    - "faster least squares approximation"

Connected through hyperedge HE-5 (induced by source paper "utility preserving secure private data release"):
  with members:
    - "membership inference attacks against machine learning models"
    - "private approximations of the 2nd moment matrix using existing techniques in linear regression"
    - "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"

Connected through hyperedge HE-6 (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-7 (induced by source paper "privacy utility trade off of linear regression under random projections and additive noise"):
  with members:
    - "differential privacy as a mutual information constraint"
    - "differentially private ordinary least squares"
    - "randomized sketches of convex programs with sharp guarantees"
    - "privacy via the johnson lindenstrauss transform"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-8 (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"

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)