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: "structure aware sampling flexible and accurate summarization"
  Abstract: In processing large quantities of data, a fundamental problem is to obtain a summary which supports approximate query answering. Random sampling yields flexible summaries which naturally support subset-sum queries with unbiased estimators and well-understood confidence bounds. #R##N#Classic sample-based summaries, however, are designed for arbitrary subset queries and are oblivious to the structure in the set of keys. The particular structure, such as hierarchy, order, or product space (multi-dimensional), makes range queries much more relevant for most analysis of the data. #R##N#Dedicated summarization algorithms for range-sum queries have also been extensively studied. They can outperform existing sampling schemes in terms of accuracy on range queries per summary size. Their accuracy, however, rapidly degrades when, as is often the case, the query spans multiple ranges. They are also less flexible - being targeted for range sum queries alone - and are often quite costly to build and use. #R##N#In this paper we propose and evaluate variance optimal sampling schemes that are structure-aware. These summaries improve over the accuracy of existing structure-oblivious sampling schemes on range queries while retaining the benefits of sample-based summaries: flexible summaries, with high accuracy on both range queries and arbitrary subset queries.

Connected through hyperedge HE-1 (induced by source paper "storyboard optimizing precomputed summaries for aggregation"):
  with members:
    - "hillview a trillion cell spreadsheet for big data"
    - "ddsketch a fast and fully mergeable quantile sketch with relative error guarantees"
    - "moment based quantile sketches for efficient high cardinality aggregation queries"
    - "macrobase prioritizing attention in fast data"
    - "optimal quantile approximation in streams"
    - "coresets and sketches"
    - "unbiased matrix rounding"

Connected through hyperedge HE-2 (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"
    - "lower bounds for sparse recovery"

Connected through hyperedge HE-3 (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"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"

Connected through hyperedge HE-4 (induced by source paper "estimating frequency statistics through distinct count measurements"):
  with members:
    - "multi objective weighted sampling"
    - "stream sampling for frequency cap statistics"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "coordinated weighted sampling for estimating aggregates over multiple weight assignments"
    - "stream sampling for variance optimal estimation of subset sums"

Connected through hyperedge HE-5 (induced by source paper "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"):
  with members:
    - "computing classic closeness centrality at scale"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "stream sampling for variance optimal estimation of subset sums"
    - "fast approximation of centrality"

Connected through hyperedge HE-6 (induced by source paper "epsilon samples of kernels"):
  with members:
    - "constructive discrepancy minimization by walking on the edges"
    - "the vc dimension of queries and selectivity estimation through sampling"
    - "constructive algorithms for discrepancy minimization"
    - "algorithms for eps approximations of terrains"

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)