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: "tight lower bound for linear sketches of moments"
  Abstract: The problem of estimating frequency moments of a data stream has attracted a lot of attention since the onset of streaming algorithms [AMS99]. While the space complexity for approximately computing the $p^{\rm th}$ moment, for $p\in(0,2]$ has been settled [KNW10], for $p>2$ the exact complexity remains open. For $p>2$ the current best algorithm uses $O(n^{1-2/p}\log n)$ words of space [AKO11,BO10], whereas the lower bound is of $\Omega(n^{1-2/p})$ [BJKS04]. #R##N#In this paper, we show a tight lower bound of $\Omega(n^{1-2/p}\log n)$ words for the class of algorithms based on linear sketches, which store only a sketch $Ax$ of input vector $x$ and some (possibly randomized) matrix $A$. We note that all known algorithms for this problem are linear sketches.

Connected through hyperedge HE-1 (induced by source paper "high probability frequency moment sketches"):
  with members:
    - "maximum matching in turnstile streams"
    - "taylor polynomial estimator for estimating frequency moments"
    - "how robust are linear sketches to adaptive inputs"
    - "fast moment estimation in data streams in optimal space"

Connected through hyperedge HE-2 (induced by source paper "tight bounds for linear sketches of approximate matchings"):
  with members:
    - "welfare maximization with limited interaction"
    - "maximum matching in turnstile streams"
    - "sketching as a tool for numerical linear algebra"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "how robust are linear sketches to adaptive inputs"
    - "better bounds for matchings in the streaming model"
    - "superlinear lower bounds for multipass graph processing"
    - "maximum matching in semi streaming with few passes"

Connected through hyperedge HE-3 (induced by source paper "taylor polynomial estimator for estimating frequency moments"):
  with members:
    - "approximating large frequency moments with o n 1 2 k bits"
    - "a lower bound for estimating high moments of a data stream"
    - "tight bounds for distributed functional monitoring"
    - "1 eps approximate sparse recovery"
    - "lower bounds for sparse recovery"
    - "online learning of noisy data with kernels"
    - "streaming algorithms from precision sampling"
    - "recursive sketching for frequency moments"

Connected through hyperedge HE-4 (induced by source paper "streaming symmetric norms via measure concentration"):
  with members:
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "taylor polynomial estimator for estimating frequency moments"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "sketching and embedding are equivalent for norms"
    - "approximating large frequency moments with pick and drop sampling"
    - "sketching and streaming entropy via approximation theory"

Connected through hyperedge HE-5 (induced by source paper "approximating large frequency moments with pick and drop sampling"):
  with members:
    - "how to catch l 2 heavy hitters on sliding windows"
    - "a lower bound for estimating high moments of a data stream"
    - "polynomial estimators for high frequency moments"
    - "tight bounds for distributed functional monitoring"
    - "recursive sketching for frequency moments"

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)