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: "stream sampling for variance optimal estimation of subset sums"
  Abstract: From a high volume stream of weighted items, we want to maintain a generic sample of a certain limited size $k$ that we can later use to estimate the total weight of arbitrary subsets. This is the classic context of on-line reservoir sampling, thinking of the generic sample as a reservoir. We present an efficient reservoir sampling scheme, $\varoptk$, that dominates all previous schemes in terms of estimation quality. #R##N#$\varoptk$ provides {\em variance optimal unbiased estimation of subset sums}. More precisely, if we have seen $n$ items of the stream, then for {\em any} subset size $m$, our scheme based on $k$ samples minimizes the average variance over all subsets of size $m$. In fact, the optimality is against any off-line scheme with $k$ samples tailored for the concrete set of items seen. In addition to optimal average variance, our scheme provides tighter worst-case bounds on the variance of {\em particular} subsets than previously possible. It is efficient, handling each new item of the stream in $O(\log k)$ time. Finally, it is particularly well suited for combination of samples from different streams in a distributed setting.

Connected through hyperedge HE-1 (induced by source paper "the adversarial robustness of sampling"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "introduction to online convex optimization"
    - "weighted reservoir sampling from distributed streams"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "stochastic bandits robust to adversarial corruptions"
    - "the everlasting database statistical validity at a fair price"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "adaptive online learning in dynamic environments"

Connected through hyperedge HE-2 (induced by source paper "network shrinkage estimation"):
  with members:
    - "higher order ranking and link prediction from closing triangles to closing higher order motifs"
    - "three hypergraph eigenvector centralities"
    - "on sampling from massive graph streams"
    - "representing higher order dependencies in networks"
    - "graphlet decomposition framework algorithms and applications"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "graph sample and hold a framework for big graph analytics"
    - "triadic measures on graphs the power of wedge sampling"

Connected through hyperedge HE-3 (induced by source paper "sampling sketches for concave sublinear functions of frequencies"):
  with members:
    - "universal streaming of subset norms"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "distributed representations of words and phrases and their compositionality"
    - "streaming algorithms from precision sampling"
    - "growing a tree in the forest constructing folksonomies by integrating structured metadata"

Connected through hyperedge HE-4 (induced by source paper "temporally biased sampling schemes for online model management"):
  with members:
    - "temporally biased sampling for online model management"
    - "macrobase prioritizing attention in fast data"
    - "the missing piece in complex analytics low latency scalable model management and serving with velox"
    - "weighted random sampling over data streams"

Connected through hyperedge HE-5 (induced by source paper "data streams with bounded deletions"):
  with members:
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "beating countsketch for heavy hitters in insertion streams"
    - "maximum matching in turnstile streams"
    - "stream sampling for frequency cap statistics"
    - "structure aware sampling flexible and accurate summarization"

Connected through hyperedge HE-6 (induced by source paper "perfect l_p sampling in a data stream"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "heavy hitters via cluster preserving clustering"
    - "pseudorandomness via the discrete fourier transform"
    - "beating countsketch for heavy hitters in insertion streams"
    - "stream sampling for frequency cap statistics"
    - "improved concentration bounds for count sketch"
    - "structure aware sampling flexible and accurate summarization"

Connected through hyperedge HE-7 (induced by source paper "clustering small samples with quality guarantees adaptivity with one2all pps"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a constant factor bi criteria approximation guarantee for k means"
    - "multi objective weighted sampling"
    - "the hardness of approximation of euclidean k means"
    - "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "optimal time bounds for approximate clustering"

Connected through hyperedge HE-8 (induced by source paper "estimating node similarity by sampling streaming bipartite graphs"):
  with members:
    - "stream aggregation through order sampling"
    - "on sampling from massive graph streams"
    - "graph sample and hold a framework for big graph analytics"
    - "network sampling from static to streaming graphs"
    - "streaming algorithms from precision sampling"
    - "the link prediction problem in bipartite networks"

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)