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: "optimal quantile approximation in streams"
  Abstract: This paper resolves one of the longest standing basic problems in the streaming computational model. Namely, optimal construction of quantile sketches. An $\varepsilon$ approximate quantile sketch receives a stream of items $x_1,\ldots,x_n$ and allows one to approximate the rank of any query up to additive error $\varepsilon n$ with probability at least $1-\delta$. The rank of a query $x$ is the number of stream items such that $x_i \le x$. The minimal sketch size required for this task is trivially at least $1/\varepsilon$. Felber and Ostrovsky obtain a $O((1/\varepsilon)\log(1/\varepsilon))$ space sketch for a fixed $\delta$. To date, no better upper or lower bounds were known even for randomly permuted streams or for approximating a specific quantile, e.g.,\ the median. This paper obtains an $O((1/\varepsilon)\log \log (1/\delta))$ space sketch and a matching lower bound. This resolves the open problem and proves a qualitative gap between randomized and deterministic quantile sketching. One of our contributions is a novel representation and modification of the widely used merge-and-reduce construction. This subtle modification allows for an analysis which is both tight and extremely simple. Similar techniques should be useful for improving other sketching objectives and geometric coreset constructions.

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"
    - "coresets and sketches"
    - "structure aware sampling flexible and accurate summarization"
    - "unbiased matrix rounding"

Connected through hyperedge HE-2 (induced by source paper "on the power of multiple anonymous messages"):
  with members:
    - "improved summation from shuffling"
    - "separating local shuffled differential privacy via histograms"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "practical and robust privacy amplification with multi party differential privacy"
    - "communication complexity in locally private distribution estimation and heavy hitters"
    - "private aggregation from fewer anonymous messages"
    - "scalable and differentially private distributed aggregation in the shuffled model"

Connected through hyperedge HE-3 (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-4 (induced by source paper "streaming algorithms for bin packing and vector scheduling"):
  with members:
    - "tight lower bound for comparison based quantile summaries"
    - "a new and improved algorithm for online bin packing"
    - "fully dynamic bin packing with limited repacking"
    - "closing the gap for makespan scheduling via sparsification techniques"
    - "a logarithmic additive integrality gap for bin packing"
    - "tight bounds for online vector scheduling"
    - "polynomiality for bin packing with a constant number of item types"
    - "on the optimality of approximation schemes for the classical scheduling problem"

Connected through hyperedge HE-5 (induced by source paper "discrepancy coresets and sketches in machine learning"):
  with members:
    - "on coresets for logistic regression"
    - "near optimal coresets of kernel density estimates"
    - "improved coresets for kernel density estimates"
    - "epsilon approximations and epsilon nets"
    - "new frameworks for offline and streaming coreset constructions"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-6 (induced by source paper "an experimental evaluation of large scale gbdt systems"):
  with members:
    - "moment based quantile sketches for efficient high cardinality aggregation queries"
    - "deep forest towards an alternative to deep neural networks"
    - "xgboost a scalable tree boosting system"
    - "dimmwitted a study of main memory statistical analytics"
    - "robust logitboost and adaptive base class abc logitboost"

Connected through hyperedge HE-7 (induced by source paper "streaming quantiles algorithms with small space and update time"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "optimal tracking of distributed heavy hitters and quantiles"
    - "medians and beyond new aggregation techniques for sensor networks"

Connected through hyperedge HE-8 (induced by source paper "communication efficient weighted sampling and quantile summary for gbdt"):
  with members:
    - "streaming quantiles algorithms with small space and update time"
    - "xgboost a scalable tree boosting system"

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)