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: "streaming sums in sublinear space"
  Abstract: Given a stream $S$ of length $m$ with element frequencies $f_i$, $i\in[n]$, and a function ${g:\mathbb{N}\to\mathbb{R}}$, we consider the problem computing a $(1\pm\epsilon)$-approximation to $\sum g(f_i)$. Most previous efforts have focused on specific functions, for example the $p$-th moment $g(x)=|x|^p$ or other norms. The main contributions of this paper are the complete classification of the space necessary for approximating periodic and decreasing functions, up to polylogarithmic factors, and a sublinear space algorithm for non-monotone functions satisfying a relatively simple sufficient condition. This is the first streaming complexity characterization for functions that are not just monotonically increasing, and it is the first improvement on the complexity of general streaming frequency sums since 2010. Our sharpest results characterize nonincreasing functions in terms of the domain size \emph{and} stream length. In contrast to approximations of the frequency moments, it turns out the storage required to approximate a decreasing function depends delicately on the length of the stream. We apply our results to derive the first sublinear-space approximations to the harmonic mean, geometric mean, and the cumulative histogram of the frequencies.

Connected through hyperedge HE-1 (induced by source paper "on approximating functions of the singular values in a stream"):
  with members:
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "spectrum estimation from samples"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "taylor polynomial estimator for estimating frequency moments"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "sketching as a tool for numerical linear algebra"
    - "sketching and embedding are equivalent for norms"
    - "optimal error of query sets under the differentially private matrix mechanism"

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)