human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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 $\expect{X}$.

Includes members:
  Member "fast moment estimation in data streams in optimal space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "solving tall dense linear programs in nearly linear time")
  Member "streaming algorithms from precision sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal sketching for kronecker product regression and low rank approximation")
  Member "recursive sketching for frequency moments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sketching transformed matrices with applications to natural language processing")

Question: Please tell me which class the source paper 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)