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 rank approximation with entrywise ell_1 norm error"
  Abstract: We study the $\ell_1$-low rank approximation problem, where for a given $n \times d$ matrix $A$ and approximation factor $\alpha \geq 1$, the goal is to output a rank-$k$ matrix $\widehat{A}$ for which #R##N#$$\|A-\widehat{A}\|_1 \leq \alpha \cdot \min_{\textrm{rank-}k\textrm{ matrices}~A'}\|A-A'\|_1,$$ where for an $n \times d$ matrix $C$, we let $\|C\|_1 = \sum_{i=1}^n \sum_{j=1}^d |C_{i,j}|$. This error measure is known to be more robust than the Frobenius norm in the presence of outliers and is indicated in models where Gaussian assumptions on the noise may not apply. The problem was shown to be NP-hard by Gillis and Vavasis and a number of heuristics have been proposed. It was asked in multiple places if there are any approximation algorithms. #R##N#We give the first provable approximation algorithms for $\ell_1$-low rank approximation, showing that it is possible to achieve approximation factor $\alpha = (\log d) \cdot \mathrm{poly}(k)$ in $\mathrm{nnz}(A) + (n+d) \mathrm{poly}(k)$ time, where $\mathrm{nnz}(A)$ denotes the number of non-zero entries of $A$. If $k$ is constant, we further improve the approximation ratio to $O(1)$ with a $\mathrm{poly}(nd)$-time algorithm. Under the Exponential Time Hypothesis, we show there is no $\mathrm{poly}(nd)$-time algorithm achieving a $(1+\frac{1}{\log^{1+\gamma}(nd)})$-approximation, for $\gamma > 0$ an arbitrarily small constant, even when $k = 1$. #R##N#We give a number of additional results for $\ell_1$-low rank approximation: nearly tight upper and lower bounds for column subset selection, CUR decompositions, extensions to low rank approximation with respect to $\ell_p$-norms for $1 \leq p < 2$ and earthmover distance, low-communication distributed protocols and low-memory streaming algorithms, algorithms with limited randomness, and bicriteria algorithms. We also give a preliminary empirical evaluation.

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 "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-3 (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-4 (induced by source paper "streaming balanced clustering"):
  with members:
    - "a constant parameterized approximation for hard capacitated k means"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "constant factor fpt approximation for capacitated k median"
    - "nearly optimal dynamic k means clustering for high dimensional data"
    - "relative error tensor low rank approximation"
    - "clustering high dimensional dynamic data streams"
    - "distributed low rank approximation of implicit functions of a matrix"
    - "new frameworks for offline and streaming coreset constructions"

Connected through hyperedge HE-5 (induced by source paper "towards a theoretical understanding of hashing based neural nets"):
  with members:
    - "on the convergence rate of training recurrent neural networks"
    - "adversarial examples from cryptographic pseudo random generators"
    - "spectral signatures in backdoor attacks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "the computational complexity of training relu s"
    - "adversarially robust generalization requires more data"
    - "a convergence theory for deep learning via over parameterization"
    - "relative error tensor low rank approximation"

Connected through hyperedge HE-6 (induced by source paper "nonlinear inductive matrix completion based on one layer neural networks"):
  with members:
    - "learning one convolutional layer with overlapping patches"
    - "learning one hidden layer neural networks with landscape design"
    - "relative error tensor low rank approximation"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "when is a convolutional filter easy to learn"

Connected through hyperedge HE-7 (induced by source paper "nearly optimal dynamic k means clustering for high dimensional data"):
  with members:
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "relative error tensor low rank approximation"
    - "clustering high dimensional dynamic data streams"
    - "new frameworks for offline and streaming coreset constructions"
    - "local search yields a ptas for k means in doubling metrics"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "heavy hitters via cluster preserving clustering"
    - "optimal principal component analysis in distributed and streaming models"

Connected through hyperedge HE-8 (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"
    - "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"
    - "the fast cauchy transform and faster robust linear regression"
    - "near optimal column based matrix reconstruction"

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)