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 "differentially private linear algebra in the streaming model"):
  Abstract: Numerical linear algebra plays an important role in computer science. In this paper, we initiate the study of performing linear algebraic tasks while preserving privacy when the data is streamed online. Our main focus is the space requirement of the privacy-preserving data-structures. We give the first {\em sketch-based} algorithm for differential privacy. We give optimal, up to logarithmic factor, space data-structures that can compute low rank approximation, linear regression, and matrix multiplication, while preserving differential privacy with better additive error bounds compared to the known results. Notably, we match the best known space bound in the non-private setting by Kane and Nelson (J. ACM, 61(1):4). #R##N#Our mechanism for differentially private low-rank approximation {\em reuses} the random Gaussian matrix in a specific way to provide a single-pass mechanism. We prove that the resulting distribution also preserve differential privacy. This can be of independent interest. We do not make any assumptions, like singular value separation or normalized row assumption, as made in the earlier works. The mechanisms for matrix multiplication and linear regression can be seen as the private analogues of the known non-private algorithms. All our mechanisms, in the form presented, can also be computed in the distributed setting.

Includes members:
  Member "circulant matrices and differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mvg mechanism differential privacy under matrix valued query")
  Member "privacy via the johnson lindenstrauss transform":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interpretable and differentially private predictions")
  Member "the noisy power method a meta algorithm with applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  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 "exploring compact reinforcement learning representations with linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dora the explorer directed outreaching reinforcement action 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)