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 moment estimation in data streams in optimal space"
  Abstract: We give a space-optimal algorithm with update time O(log^2(1/eps)loglog(1/eps)) for (1+eps)-approximating the pth frequency moment, 0 < p < 2, of a length-n vector updated in a data stream. This provides a nearly exponential improvement in the update time complexity over the previous space-optimal algorithm of [Kane-Nelson-Woodruff, SODA 2010], which had update time Omega(1/eps^2).

Connected through hyperedge HE-1 (induced by source paper "solving tall dense linear programs in nearly linear time"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "dynamic matrix inverse improved algorithms and matching conditional lower bounds"
    - "dynamic streaming spectral sparsification in nearly linear time and space"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "a deterministic linear program solver in current matrix multiplication time"
    - "solving linear programs in the current matrix multiplication time"
    - "relative error tensor low rank approximation"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"

Connected through hyperedge HE-2 (induced by source paper "optimal sketching for kronecker product regression and low rank approximation"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "almost optimal tensor sketch"
    - "oblivious sketching of high degree polynomial kernels"
    - "a deterministic linear program solver in current matrix multiplication time"
    - "tight bounds for ell_p oblivious subspace embeddings"
    - "solving linear programs in the current matrix multiplication time"
    - "towards a zero one law for entrywise low rank approximation"
    - "sublinear time low rank approximation of distance matrices"

Connected through hyperedge HE-3 (induced by source paper "towards optimal moment estimation in streaming and distributed models"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "weighted reservoir sampling from distributed streams"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "distributed statistical estimation of matrix products with applications"
    - "revisiting frequency moment estimation in random order streams"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "continuous monitoring of ell_p norms in data streams"
    - "communication optimal distributed clustering"

Connected through hyperedge HE-4 (induced by source paper "tight bounds for the subspace sketch problem with applications"):
  with members:
    - "distributed statistical estimation of matrix products with applications"
    - "high probability frequency moment sketches"
    - "optimal lower bounds for sketching graph cuts"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "on sketching quadratic forms"
    - "sketching as a tool for numerical linear algebra"
    - "on the communication complexity of linear algebraic problems in the message passing model"
    - "quantile regression for large scale applications"

Connected through hyperedge HE-5 (induced by source paper "separating k player from t player one way communication with applications to data streams"):
  with members:
    - "tight bounds for distributed functional monitoring"

Connected through hyperedge HE-6 (induced by source paper "universal streaming of subset norms"):
  with members:
    - "revisiting frequency moment estimation in random order streams"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "streaming symmetric norms via measure concentration"
    - "on sketching quadratic forms"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "an optimal lower bound on the communication complexity of gap hamming distance"
    - "streaming algorithms from precision sampling"

Connected through hyperedge HE-7 (induced by source paper "tracking the ell_2 norm with constant update time"):
  with members:
    - "beating countsketch for heavy hitters in insertion streams"
    - "time lower bounds for nonadaptive turnstile streaming algorithms"
    - "tracking the frequency moments at all times"
    - "a sparse johnson lindenstrauss transform"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-8 (induced by source paper "high probability frequency moment sketches"):
  with members:
    - "maximum matching in turnstile streams"
    - "taylor polynomial estimator for estimating frequency moments"
    - "tight lower bound for linear sketches of moments"
    - "how robust are linear sketches to adaptive inputs"

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)