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: "polynomial estimators for high frequency moments"
  Abstract: We present an algorithm for computing $F_p$, the $p$th moment of an $n$-dimensional frequency vector of a data stream, for $2 < p < \log (n) $, to within $1\pm \epsilon$ factors, $\epsilon \in [n^{-1/p},1]$ with high constant probability. Let $m$ be the number of stream records and $M$ be the largest magnitude of a stream update. #R##N#The algorithm uses space in bits $$ O(p^2\epsilon^{-2}n^{1-2/p}E(p,n) \log (n) \log (nmM)/\min(\log (n),\epsilon^{4/p-2}))$$ where, $E(p,n) = (1-2/p)^{-1}(1-n^{-4(1-2/p})$. Here $E(p,n)$ is $ O(1)$ for $p = 2+\Omega(1)$ and $ O(\log n)$ for $p = 2 + O(1/\log (n)$. This improves upon the space required by current algorithms \cite{iw:stoc05,bgks:soda06,ako:arxiv10,bo:arxiv10} by a factor of at least $\Omega(\epsilon^{-4/p} \min(\log (n), \epsilon^{4/p-2}))$. The update time is $O(\log (n))$. We use a new technique for designing estimators for functions of the form $\psi(\expect{X})$, where, $X$ is a random variable and $\psi$ is a smooth function, based on a low-degree Taylor polynomial expansion of $\psi(\expect{X})$ around an estimate of $\expect{X}$.

Connected through hyperedge HE-1 (induced by source paper "approximating large frequency moments with o n 1 2 k bits"):
  with members:
    - "a lower bound for estimating high moments of a data stream"
    - "tight bounds for distributed functional monitoring"
    - "streaming algorithms from precision sampling"
    - "recursive sketching for frequency moments"
    - "asymptotically optimal lower bounds on the nih multi party information"

Connected through hyperedge HE-2 (induced by source paper "universal sketches for the frequency negative moments and other decreasing streaming sums"):
  with members:
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "recursive sketching for frequency moments"
    - "sketching and streaming entropy via approximation theory"

Connected through hyperedge HE-3 (induced by source paper "streaming sums in sublinear space"):
  with members:
    - "approximating large frequency moments with o n 1 2 k bits"
    - "streaming algorithms from precision sampling"
    - "recursive sketching for frequency moments"

Connected through hyperedge HE-4 (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"
    - "tight lower bound for linear sketches of moments"
    - "a lower bound for estimating high moments of a data stream"
    - "tight bounds for distributed functional monitoring"
    - "recursive sketching for frequency moments"

Connected through hyperedge HE-5 (induced by source paper "tight bounds for distributed functional monitoring"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "a lower bound for estimating high moments of a data stream"
    - "information complexity versus corruption and applications to orthogonality and gap hamming"
    - "randomized algorithms for tracking distributed count frequencies and ranks"
    - "lower bounds for number in hand multiparty communication complexity made easy"
    - "information cost tradeoffs for augmented index and streaming language recognition"
    - "fast moment estimation in data streams in optimal space"
    - "an optimal lower bound on the communication complexity of gap hamming distance"

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)