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: "fast and space optimal low rank factorization in the streaming model with application in differential privacy"
  Abstract: In this paper, we consider the problem of computing a low-rank factorization of an $m \times n$ matrix in the general turnstile update model. We consider both the private and non-private setting. In the non-private setting, we give a space-optimal algorithm that computes a low-rank factorization. Our algorithm maintains three sketches of the matrix instead of five as in Boutsidis {\it et al.} (STOC 2016). Our algorithm takes $\widetilde{O}(1)$ time to update the sketch and computes the factorization in time linear in the sparsity and the dimensions of the matrix. In the private setting, we study low-rank factorization in the framework of differential privacy and under turnstile updates. We give two algorithms with respect to two levels of privacy. Both of our privacy levels are stronger than earlier studied privacy levels, namely that of Blocki {\it et al.} (FOCS 2012), Dwork {\it et al.} (STOC 2014), Hardt and Roth (STOC 2012, STOC 2013), and Hardt and Price (NIPS 2014).

Connected through hyperedge HE-1 (induced by source paper "streaming low rank matrix approximation with an application to scientific simulation"):
  with members:
    - "low rank tucker approximation of a tensor from streaming data"
    - "practical sketching algorithms for low rank matrix approximation"
    - "randomized single view algorithms for low rank matrix approximation"
    - "parallel tensor compression for large scale scientific data"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal principal component analysis in distributed and streaming models"
    - "improved bounds on sample size for implicit matrix trace estimators"

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)