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: "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
  Abstract: Low-distortion embeddings are critical building blocks for developing random sampling and random projection algorithms for linear algebra problems. We show that, given a matrix $A \in \R^{n \times d}$ with $n \gg d$ and a $p \in [1, 2)$, with a constant probability, we can construct a low-distortion embedding matrix $\Pi \in \R^{O(\poly(d)) \times n}$ that embeds $\A_p$, the $\ell_p$ subspace spanned by $A$'s columns, into $(\R^{O(\poly(d))}, \| \cdot \|_p)$; the distortion of our embeddings is only $O(\poly(d))$, and we can compute $\Pi A$ in $O(\nnz(A))$ time, i.e., input-sparsity time. Our result generalizes the input-sparsity time $\ell_2$ subspace embedding by Clarkson and Woodruff [STOC'13]; and for completeness, we present a simpler and improved analysis of their construction for $\ell_2$. These input-sparsity time $\ell_p$ embeddings are optimal, up to constants, in terms of their running time; and the improved running time propagates to applications such as $(1\pm \epsilon)$-distortion $\ell_p$ subspace embedding and relative-error $\ell_p$ regression. For $\ell_2$, we show that a $(1+\epsilon)$-approximate solution to the $\ell_2$ regression problem specified by the matrix $A$ and a vector $b \in \R^n$ can be computed in $O(\nnz(A) + d^3 \log(d/\epsilon) /\epsilon^2)$ time; and for $\ell_p$, via a subspace-preserving sampling procedure, we show that a $(1\pm \epsilon)$-distortion embedding of $\A_p$ into $\R^{O(\poly(d))}$ can be computed in $O(\nnz(A) \cdot \log n)$ time, and we also show that a $(1+\epsilon)$-approximate solution to the $\ell_p$ regression problem $\min_{x \in \R^d} \|A x - b\|_p$ can be computed in $O(\nnz(A) \cdot \log n + \poly(d) \log(1/\epsilon)/\epsilon^2)$ time. Moreover, we can improve the embedding dimension or equivalently the sample size to $O(d^{3+p/2} \log(1/\epsilon) / \epsilon^2)$ without increasing the complexity.

Connected through hyperedge HE-1 (induced by source paper "sketching transformed matrices with applications to natural language processing"):
  with members:
    - "solving tall dense linear programs in nearly linear time"
    - "tight bounds for the subspace sketch problem with applications"
    - "optimal sketching for kronecker product regression and low rank approximation"
    - "a near optimal algorithm for approximating the john ellipsoid"
    - "regularized weighted low rank approximation"
    - "robust and sample optimal algorithms for psd low rank approximation"
    - "dimensionality reduction for tukey regression"
    - "total least squares regression in input sparsity time"

Connected through hyperedge HE-2 (induced by source paper "speeding up linear programming using randomized linear algebra"):
  with members:
    - "solving tall dense linear programs in nearly linear time"
    - "solving linear programs with sqrt rank linear system solves"
    - "solving linear programs in the current matrix multiplication time"
    - "optimal approximate matrix product in terms of stable rank"
    - "efficient inverse maintenance and faster algorithms for linear programming"
    - "sketching as a tool for numerical linear algebra"
    - "path finding ii an o m sqrt n algorithm for the minimum cost flow problem"
    - "matching the universal barrier without paying the costs solving linear programs with o sqrt rank linear system solves"

Connected through hyperedge HE-3 (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-4 (induced by source paper "streaming low rank matrix approximation with an application to scientific simulation"):
  with members:
    - "low rank tucker approximation of a tensor from streaming data"
    - "practical sketching algorithms for low rank matrix approximation"
    - "randomized single view algorithms for low rank matrix approximation"
    - "fast and space optimal low rank factorization in the streaming model with application in differential privacy"
    - "parallel tensor compression for large scale scientific data"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal principal component analysis in distributed and streaming models"

Connected through hyperedge HE-5 (induced by source paper "learning based low rank approximations"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "learning to branch"
    - "hyperspectral image dataset for benchmarking on salient object detection"
    - "competitive caching with machine learned advice"
    - "the case for learned index structures"
    - "learning combinatorial optimization algorithms over graphs"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "lazysvd even faster svd decomposition yet without agonizing pain"

Connected through hyperedge HE-6 (induced by source paper "total least squares regression in input sparsity time"):
  with members:
    - "tight bounds for the subspace sketch problem with applications"
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "optimal sketching for kronecker product regression and low rank approximation"
    - "a near optimal algorithm for approximating the john ellipsoid"
    - "dimensionality reduction for tukey regression"
    - "solving linear programs in the current matrix multiplication time"
    - "subspace embedding and linear regression with orlicz norm"
    - "relative error tensor low rank approximation"

Connected through hyperedge HE-7 (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"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "approximate near neighbors for general symmetric norms"
    - "input sparsity and hardness for robust subspace approximation"

Connected through hyperedge HE-8 (induced by source paper "strong coresets for subspace approximation and k median in nearly linear time"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "new frameworks for offline and streaming coreset constructions"
    - "input sparsity and hardness for robust subspace approximation"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"

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)