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: "tracking the frequency moments at all times"
  Abstract: The traditional requirement for a randomized streaming algorithm is just {\em one-shot}, i.e., algorithm should be correct (within the stated $\eps$-error bound) at the end of the stream. In this paper, we study the {\em tracking} problem, where the output should be correct at all times. The standard approach for solving the tracking problem is to run $O(\log m)$ independent instances of the one-shot algorithm and apply the union bound to all $m$ time instances. In this paper, we study if this standard approach can be improved, for the classical frequency moment problem. We show that for the $F_p$ problem for any $1 < p \le 2$, we actually only need $O(\log \log m + \log n)$ copies to achieve the tracking guarantee in the cash register model, where $n$ is the universe size. Meanwhile, we present a lower bound of $\Omega(\log m \log\log m)$ bits for all linear sketches achieving this guarantee. This shows that our upper bound is tight when $n=(\log m)^{O(1)}$. We also present an $\Omega(\log^2 m)$ lower bound in the turnstile model, showing that the standard approach by using the union bound is essentially optimal.

Connected through hyperedge HE-1 (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"
    - "a sparse johnson lindenstrauss transform"
    - "fast moment estimation in data streams in optimal space"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-2 (induced by source paper "approximate set union via approximate randomization"):
  with members:
    - "optimal streaming and tracking distinct elements with high probability"
    - "partial sublinear time approximation and inapproximation for maximum coverage"
    - "approximating the volume of unions and intersections of high dimensional geometric objects"

Connected through hyperedge HE-3 (induced by source paper "optimal streaming and tracking distinct elements with high probability"):
  with members:
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "explicit resilient functions matching ajtai linial"
    - "a multi round communication lower bound for gap hamming and some consequences"

Connected through hyperedge HE-4 (induced by source paper "continuous monitoring of ell_p norms in data streams"):
  with members:
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "beating countsketch for heavy hitters in insertion streams"
    - "streaming symmetric norms via measure concentration"
    - "taylor polynomial estimator for estimating frequency moments"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "fast moment estimation in data streams in optimal space"
    - "measuring independence of datasets"
    - "bounded independence fools degree 2 threshold functions"

Connected through hyperedge HE-5 (induced by source paper "beating countsketch for heavy hitters in insertion streams"):
  with members:
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "approximating large frequency moments with pick and drop sampling"
    - "low rank approximation and regression in input sparsity time"
    - "improved concentration bounds for count sketch"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "lower bounds for sparse recovery"

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)