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: "the fast cauchy transform and faster robust linear regression"
  Abstract: We provide fast algorithms for overconstrained $\ell_p$ regression and related problems: for an $n\times d$ input matrix $A$ and vector $b\in\mathbb{R}^n$, in $O(nd\log n)$ time we reduce the problem $\min_{x\in\mathbb{R}^d} \|Ax-b\|_p$ to the same problem with input matrix $\tilde A$ of dimension $s \times d$ and corresponding $\tilde b$ of dimension $s\times 1$. Here, $\tilde A$ and $\tilde b$ are a coreset for the problem, consisting of sampled and rescaled rows of $A$ and $b$; and $s$ is independent of $n$ and polynomial in $d$. Our results improve on the best previous algorithms when $n\gg d$, for all $p\in[1,\infty)$ except $p=2$. We also provide a suite of improved results for finding well-conditioned bases via ellipsoidal rounding, illustrating tradeoffs between running time and conditioning quality, including a one-pass conditioning algorithm for general $\ell_p$ problems. #R##N#We also provide an empirical evaluation of implementations of our algorithms for $p=1$, comparing them with related algorithms. Our empirical results show that, in the asymptotic regime, the theory is a very good guide to the practical performance of these algorithms. Our algorithms use our faster constructions of well-conditioned bases for $\ell_p$ spaces and, for $p=1$, a fast subspace embedding of independent interest that we call the Fast Cauchy Transform: a distribution over matrices $\Pi:\mathbb{R}^n\mapsto \mathbb{R}^{O(d\log d)}$, found obliviously to $A$, that approximately preserves the $\ell_1$ norms: that is, with large probability, simultaneously for all $x$, $\|Ax\|_1 \approx \|\Pi Ax\|_1$, with distortion $O(d^{2+\eta})$, for an arbitrarily small constant $\eta>0$; and, moreover, $\Pi A$ can be computed in $O(nd\log d)$ time. The techniques underlying our Fast Cauchy Transform include fast Johnson-Lindenstrauss transforms, low-coherence matrices, and rescaling by Cauchy random variables.

Connected through hyperedge HE-1 (induced by source paper "optimal sketching for kronecker product regression and low rank approximation"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "almost optimal tensor sketch"
    - "oblivious sketching of high degree polynomial kernels"
    - "a deterministic linear program solver in current matrix multiplication time"
    - "tight bounds for ell_p oblivious subspace embeddings"
    - "solving linear programs in the current matrix multiplication time"
    - "towards a zero one law for entrywise low rank approximation"
    - "sublinear time low rank approximation of distance matrices"

Connected through hyperedge HE-2 (induced by source paper "dimensionality reduction for tukey regression"):
  with members:
    - "tight bounds for the subspace sketch problem with applications"
    - "tight bounds for ell_p oblivious subspace embeddings"
    - "input sparsity and hardness for robust subspace approximation"
    - "robust optimization for deep regression"
    - "sketching as a tool for numerical linear algebra"
    - "quantile regression for large scale applications"
    - "low rank approximation and regression in input sparsity time"
    - "iterative row sampling"

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"
    - "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-4 (induced by source paper "fast fixed dimension l2 subspace embeddings of arbitrary accuracy with application to l1 and l2 tasks"):
  with members:
    - "fast and rip optimal transforms"
    - "quantile regression for large scale applications"
    - "subspace embeddings and ell_p regression using exponential random variables"
    - "low rank approximation and regression in input sparsity time"
    - "iterative row sampling"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "fast approximation of matrix coherence and statistical leverage"
    - "row sampling for matrix algorithms via a non commutative bernstein bound"

Connected through hyperedge HE-5 (induced by source paper "on coresets for logistic regression"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "recursive sampling for the nystr om method"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "k means for streaming and distributed big sparse data"
    - "input sparsity and hardness for robust subspace approximation"
    - "sketching as a tool for numerical linear algebra"
    - "uniform sampling for matrix approximation"
    - "subspace embeddings and ell_p regression using exponential random variables"

Connected through hyperedge HE-6 (induced by source paper "towards a zero one law for entrywise low rank approximation"):
  with members:
    - "relative error tensor low rank approximation"
    - "algorithms for ell_p low rank approximation"
    - "low rank approximation with entrywise ell_1 norm error"
    - "input sparsity and hardness for robust subspace approximation"
    - "streaming symmetric norms via measure concentration"
    - "optimal cur matrix decompositions"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-7 (induced by source paper "tight bounds for ell_p oblivious subspace embeddings"):
  with members:
    - "uniform sampling for matrix approximation"
    - "quantile regression for large scale applications"
    - "lower bounds for oblivious subspace embeddings"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "low rank approximation and regression in input sparsity time"
    - "iterative row sampling"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-8 (induced by source paper "leveraging well conditioned bases streaming distributed summaries in minkowski p norms"):
  with members:
    - "frequent directions simple and deterministic matrix sketching"
    - "sketching as a tool for numerical linear algebra"
    - "quantile regression for large scale applications"
    - "relative errors for deterministic low rank matrix approximations"
    - "how robust are linear sketches to adaptive inputs"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "on the complexity of processing massive unordered distributed data"

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)