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: "compressed matrix multiplication"
  Abstract: Motivated by the problems of computing sample covariance matrices, and of transforming a collection of vectors to a basis where they are sparse, we present a simple algorithm that computes an approximation of the product of two n-by-n real matrices A and B. Let ||AB||_F denote the Frobenius norm of AB, and b be a parameter determining the time/accuracy trade-off. Given 2-wise independent hash functions $_1,h_2: [n] -> [b], and s_1,s_2: [n] -> {-1,+1} the algorithm works by first "compressing" the matrix product into the polynomial p(x) = sum_{k=1}^n (sum_{i=1}^n A_{ik} s_1(i) x^{h_1(i)}) (sum_{j=1}^n B_{kj} s_2(j) x^{h_2(j)}) #R##N#Using FFT for polynomial multiplication, we can compute c_0,...,c_{b-1} such that sum_i c_i x^i = (p(x) mod x^b) + (p(x) div x^b) in time \~O(n^2+ n b). #R##N#An unbiased estimator of (AB)_{ij} with variance at most ||AB||_F^2 / b can then be computed as: #R##N#C_{ij} = s_1(i) s_2(j) c_{(h_1(i)+h_2(j)) mod b. #R##N#Our approach also leads to an algorithm for computing AB exactly, whp., in time \~O(N + nb) in the case where A and B have at most N nonzero entries, and AB has at most b nonzero entries. #R##N#Also, we use error-correcting codes in a novel way to recover significant entries of AB in near-linear time.

Connected through hyperedge HE-1 (induced by source paper "solving tall dense linear programs in nearly linear time"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "dynamic matrix inverse improved algorithms and matching conditional lower bounds"
    - "dynamic streaming spectral sparsification in nearly linear time and space"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "a deterministic linear program solver in current matrix multiplication time"
    - "solving linear programs in the current matrix multiplication time"
    - "relative error tensor low rank approximation"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"

Connected through hyperedge HE-2 (induced by source paper "oblivious sketching of high degree polynomial kernels"):
  with members:
    - "almost optimal tensor sketch"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "recursive sampling for the nystr om method"
    - "optimal approximate matrix product in terms of stable rank"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-3 (induced by source paper "the gaussiansketch for almost relative error kernel distance"):
  with members:
    - "hashing based estimators for kernel density in high dimensions"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "locality sensitive hashing of curves"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "is input sparsity time possible for kernel low rank approximation"
    - "optimality of the johnson lindenstrauss lemma"
    - "streaming kernel principal component analysis"
    - "fastmmd ensemble of circular discrepancy for efficient two sample test"

Connected through hyperedge HE-4 (induced by source paper "fast randomized matrix and tensor interpolative decomposition using countsketch"):
  with members:
    - "a practical randomized cp tensor decomposition"
    - "sketching for kronecker product regression and p splines"
    - "fast randomized algorithms for t product based tensor operations and decompositions with applications to imaging data"
    - "optimal cur matrix decompositions"
    - "low rank approximation and regression in input sparsity time"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"
    - "cur from a sparse optimization viewpoint"

Connected through hyperedge HE-5 (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"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "lu factorization with errors"):
  with members:
    - "error correction in fast matrix multiplication and inverse"
    - "what can and can t we do with sparse polynomials"
    - "certificates for triangular equivalence and rank profiles"
    - "linear time interactive certificates for the minimal polynomial and the determinant of a sparse matrix"
    - "efficiently correcting matrix products"
    - "essentially optimal interactive certificates in linear algebra"
    - "powers of tensors and fast matrix multiplication"
    - "parallel computation of echelon forms"

Connected through hyperedge HE-7 (induced by source paper "interaction hard thresholding consistent sparse quadratic regression in sub quadratic time and space"):
  with members:
    - "high dimensional robust sparse regression"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "distributed pcp theorems for hardness of approximation in p"
    - "nonconvex sparse learning via stochastic optimization with progressive variance reduction"
    - "a greedy approach for budgeted maximum inner product search"
    - "diamond sampling for approximate maximum all pairs dot product mad search"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "sparse polynomial learning and graph sketching"

Connected through hyperedge HE-8 (induced by source paper "randomization of approximate bilinear computation for matrix multiplication"):
  with members:
    - "training deep neural networks with 8 bit floating point numbers"
    - "implementing strassen s algorithm with cutlass on nvidia volta gpus"
    - "improving the numerical stability of fast matrix multiplication"
    - "deep learning with limited numerical precision"
    - "a framework for practical parallel fast matrix multiplication"

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)