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: "sketching and streaming entropy via approximation theory"
  Abstract: We conclude a sequence of work by giving near-optimal sketching and streaming algorithms for estimating Shannon entropy in the most general streaming model, with arbitrary insertions and deletions. This improves on prior results that obtain suboptimal space bounds in the general model, and near-optimal bounds in the insertion-only model without sketching. Our high-level approach is simple: we give algorithms to estimate Renyi and Tsallis entropy, and use them to extrapolate an estimate of Shannon entropy. The accuracy of our estimates is proven using approximation theory arguments and extremal properties of Chebyshev polynomials, a technique which may be useful for other problems. Our work also yields the best-known and near-optimal additive approximations for entropy, and hence also for conditional entropy and mutual information.

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 "estimating entropy of distributions in constant space"):
  with members:
    - "communication and memory efficient testing of discrete distributions"
    - "efficient profile maximum likelihood for universal symmetric property estimation"
    - "detecting correlations with little memory and communication"
    - "minimax optimal estimators for additive scalar functionals of discrete distributions"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "minimax estimation of functionals of discrete distributions"

Connected through hyperedge HE-3 (induced by source paper "randomized linear algebra approaches to estimate the von neumann entropy of density matrices"):
  with members:
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"
    - "large scale log determinant computation through stochastic chebyshev expansions"
    - "improved bounds on sample size for implicit matrix trace estimators"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "faster least squares approximation"

Connected through hyperedge HE-4 (induced by source paper "nearly optimal distinct elements and heavy hitters on sliding windows"):
  with members:
    - "optimal streaming and tracking distinct elements with high probability"
    - "continuous monitoring of ell_p norms in data streams"
    - "submodular optimization over sliding windows"
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "submodular maximization over sliding windows"
    - "heavy hitters via cluster preserving clustering"
    - "beating countsketch for heavy hitters in insertion streams"
    - "how to catch l 2 heavy hitters on sliding windows"

Connected through hyperedge HE-5 (induced by source paper "spectrum approximation beyond fast matrix multiplication algorithms and hardness"):
  with members:
    - "fast estimation of approximate matrix ranks using spectral densities"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "embeddings of schatten norms with applications to data streams"
    - "improved stochastic trace estimation using mutually unbiased bases"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "sketches for matrix norms faster smaller and more general"
    - "solving ridge regression using sketched preconditioned svrg"
    - "faster eigenvector computation via shift and invert preconditioning"

Connected through hyperedge HE-6 (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-7 (induced by source paper "polynomial representations of threshold functions and algorithmic applications"):
  with members:
    - "average case lower bounds and satisfiability algorithms for small threshold circuits"
    - "subquadratic algorithms for succinct stable matching"
    - "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"
    - "optimal data dependent hashing for approximate near neighbors"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "new algorithms and lower bounds for circuits with linear threshold gates"
    - "beyond locality sensitive hashing"
    - "faster all pairs shortest paths via circuit complexity"

Connected through hyperedge HE-8 (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"
    - "approximating large frequency moments with pick and drop 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)