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: "streaming space complexity of nearly all functions of one variable on frequency vectors"
  Abstract: A central problem in the theory of algorithms for data streams is to determine which functions on a stream can be approximated in sublinear, and especially sub-polynomial or poly-logarithmic, space. Given a function $g$, we study the space complexity of approximating $\sum_{i=1}^n g(|f_i|)$, where $f\in\mathbb{Z}^n$ is the frequency vector of a turnstile stream. This is a generalization of the well-known frequency moments problem, and previous results apply only when $g$ is monotonic or has a special functional form. Our contribution is to give a condition such that, except for a narrow class of functions $g$, there is a space-efficient approximation algorithm for the sum if and only if $g$ satisfies the condition. The functions $g$ that we are able to characterize include all convex, concave, monotonic, polynomial, and trigonometric functions, among many others, and is the first such characterization for non-monotonic functions. Thus, for nearly all functions of one variable, we answer the open question from the celebrated paper of Alon, Matias and Szegedy (1996).

Connected through hyperedge HE-1 (induced by source paper "towards optimal moment estimation in streaming and distributed models"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "weighted reservoir sampling from distributed streams"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "distributed statistical estimation of matrix products with applications"
    - "revisiting frequency moment estimation in random order streams"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "continuous monitoring of ell_p norms in data streams"
    - "communication optimal distributed clustering"

Connected through hyperedge HE-2 (induced by source paper "efficient symmetric norm regression via linear sketching"):
  with members:
    - "on mean estimation for general norms with statistical queries"
    - "dimensionality reduction for tukey regression"
    - "tight bounds for ell_p oblivious subspace embeddings"
    - "subspace embedding and linear regression with orlicz norm"
    - "algorithmic theory of odes and sampling from well conditioned logconcave densities"
    - "approximate near neighbors for general symmetric norms"
    - "input sparsity and hardness for robust subspace approximation"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"

Connected through hyperedge HE-3 (induced by source paper "tight bounds for the subspace sketch problem with applications"):
  with members:
    - "distributed statistical estimation of matrix products with applications"
    - "high probability frequency moment sketches"
    - "optimal lower bounds for sketching graph cuts"
    - "on sketching quadratic forms"
    - "sketching as a tool for numerical linear algebra"
    - "on the communication complexity of linear algebraic problems in the message passing model"
    - "quantile regression for large scale applications"
    - "the fast cauchy transform and faster robust linear regression"

Connected through hyperedge HE-4 (induced by source paper "sampling sketches for concave sublinear functions of frequencies"):
  with members:
    - "universal streaming of subset norms"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "distributed representations of words and phrases and their compositionality"
    - "streaming algorithms from precision sampling"
    - "growing a tree in the forest constructing folksonomies by integrating structured metadata"
    - "stream sampling for variance optimal estimation of subset sums"

Connected through hyperedge HE-5 (induced by source paper "distributed data summarization in well connected networks"):
  with members:
    - "new distributed algorithms in almost mixing time via transformations from parallel algorithms"
    - "optimal gossip algorithms for exact and approximate quantile computations"
    - "taylor polynomial estimator for estimating frequency moments"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "a multi round communication lower bound for gap hamming and some consequences"

Connected through hyperedge HE-6 (induced by source paper "universal streaming of subset norms"):
  with members:
    - "revisiting frequency moment estimation in random order streams"
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "streaming symmetric norms via measure concentration"
    - "on sketching quadratic forms"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "fast moment estimation in data streams in optimal space"
    - "an optimal lower bound on the communication complexity of gap hamming distance"
    - "streaming algorithms from precision sampling"

Connected through hyperedge HE-7 (induced by source paper "optimal streaming and tracking distinct elements with high probability"):
  with members:
    - "explicit resilient functions matching ajtai linial"
    - "tracking the frequency moments at all times"
    - "a multi round communication lower bound for gap hamming and some consequences"

Connected through hyperedge HE-8 (induced by source paper "on approximating functions of the singular values in a stream"):
  with members:
    - "spectrum estimation from samples"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "taylor polynomial estimator for estimating frequency moments"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "sketching as a tool for numerical linear algebra"
    - "sketching and embedding are equivalent for norms"
    - "streaming sums in sublinear space"
    - "optimal error of query sets under the differentially private matrix mechanism"

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)