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: "approximating large frequency moments with pick and drop sampling"
  Abstract: Given data stream $D = \{p_1,p_2,...,p_m\}$ of size $m$ of numbers from $\{1,..., n\}$, the frequency of $i$ is defined as $f_i = |\{j: p_j = i\}|$. The $k$-th \emph{frequency moment} of $D$ is defined as $F_k = \sum_{i=1}^n f_i^k$. We consider the problem of approximating frequency moments in insertion-only streams for $k\ge 3$. For any constant $c$ we show an $O(n^{1-2/k}\log(n)\log^{(c)}(n))$ upper bound on the space complexity of the problem. Here $\log^{(c)}(n)$ is the iterative $\log$ function. To simplify the presentation, we make the following assumptions: $n$ and $m$ are polynomially far; approximation error $\epsilon$ and parameter $k$ are constants. We observe a natural bijection between streams and special matrices. Our main technical contribution is a non-uniform sampling method on matrices. We call our method a \emph{pick-and-drop sampling}; it samples a heavy element (i.e., element $i$ with frequency $\Omega(F_k)$) with probability $\Omega(1/n^{1-2/k})$ and gives approximation $\tilde{f_i} \ge (1-\epsilon)f_i$. In addition, the estimations never exceed the real values, that is $ \tilde{f_j} \le f_j$ for all $j$. As a result, we reduce the space complexity of finding a heavy element to $O(n^{1-2/k}\log(n))$ bits. We apply our method of recursive sketches and resolve the problem with $O(n^{1-2/k}\log(n)\log^{(c)}(n))$ bits.

Connected through hyperedge HE-1 (induced by source paper "heavy hitters via cluster preserving clustering"):
  with members:
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "beating countsketch for heavy hitters in insertion streams"
    - "linear time list recovery of high rate expander codes"
    - "taylor polynomial estimator for estimating frequency moments"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "partitioning well clustered graphs spectral clustering works"
    - "constant factor approximation for balanced cut in the pie model"
    - "flow based algorithms for local graph clustering"

Connected through hyperedge HE-2 (induced by source paper "beating countsketch for heavy hitters in insertion streams"):
  with members:
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "tracking the frequency moments at all times"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "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"

Connected through hyperedge HE-3 (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"
    - "tight lower bound for linear sketches of moments"
    - "sketching and streaming entropy via approximation theory"

Connected through hyperedge HE-4 (induced by source paper "catching the head tail and everything in between a streaming algorithm for the degree distribution"):
  with members:
    - "graph sample and hold a framework for big graph analytics"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "network sampling from static to streaming graphs"
    - "community structure and scale free collections of erdős renyi graphs"
    - "space efficient sampling from social activity streams"

Connected through hyperedge HE-5 (induced by source paper "tight lower bound for linear sketches of moments"):
  with members:
    - "fast moment estimation in data streams in optimal space"
    - "streaming algorithms from precision sampling"

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)