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 "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.

Includes members:
  Member "restricted isometry property for general p norms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "differentially private linear algebra in the streaming model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the price of differential privacy for low rank factorization")
  Member "privacy via the johnson lindenstrauss transform":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interpretable and differentially private predictions")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "locally private hypothesis selection")
  Member "differentially private data analysis of social networks via restricted sensitivity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "private identity testing for high dimensional distributions")
  Member "the johnson lindenstrauss transform itself preserves differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "crypt epsilon crypto assisted differential privacy on untrusted servers")
  Member "beating randomized response on incoherent matrices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mvg mechanism differential privacy under matrix valued query")
  Member "osnap faster numerical linear algebra algorithms via sparser subspace embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sketching transformed matrices with applications to natural language processing")

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)