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 "efficient differentially private f_0 linear sketching"):
  Abstract: A powerful feature of linear sketches is that from sketches of two data vectors, one can compute the sketch of the difference between the vectors. This allows us to answer fine-grained questions about the difference between two data sets. In this work, we consider how to achieve this kind of property with sketches that are differentially private. We describe how to compute linear sketches for $F_0$, the number of distinct elements, that can efficiently be made differentially private. Specifically, we consider a sketch that is linear over GF(2), mapping a vector $x\in \{0,1\}^u$ to $Hx\in\{0,1\}^\tau$ for a matrix $H$ sampled from a suitable distribution $\mathcal{H}$. Differential privacy is achieved by using randomized response, flipping each bit of $Hx$ with probability $p<1/2$. That is, for a vector $\varphi \in \{0,1\}^\tau$ where $\Pr[(\varphi)_j = 1] = p$ independently for each entry $j$, we consider the noisy sketch $Hx + \varphi$, where the addition of noise happens over GF(2). We show that for every choice of $0<\beta, \varepsilon < 1$ there exists $p<1/2$ and a distribution $\mathcal{H}$ of linear sketches of size $\tau = O(\log^4(u)\varepsilon^{-2}\beta^{-2})$ such that: 1) For random $H\sim\mathcal{H}$ and noise vector $\varphi$, given $Hx + \varphi$ we can compute an estimate of $\Vert x\Vert_0$ that is accurate within a factor $1\pm\beta$, plus an additive error $O(\log^3(u)\varepsilon^{-2}\beta^{-2})$, with high probability, and 2) For every $H\sim\mathcal{H}$, $Hx + \varphi$ is $\varepsilon$-differentially private over the randomness in $\varphi$. Previously, Mir et al.~(PODS 2011) had described a private way of sketching $F_0$, but their noise vector $\varphi$ is constructed using the exponential mechanism, and is not computationally efficient (quasipolynomial time in the sketch size)...

Includes members:
  Member "cardinality estimators do not preserve privacy":
    also connected through other hyperedges:
      (none)
  Member "efficient private statistics with succinct sketches":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "rappor randomized aggregatable privacy preserving ordinal response":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locally private hypothesis selection")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "locally private hypothesis selection")

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.DS(Data Structures and Algorithms)