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: "towards a zero one law for entrywise low rank approximation"
  Abstract: There are a number of approximation algorithms for NP-hard versions of low rank approximation, such as finding a rank-$k$ matrix $B$ minimizing the sum of absolute values of differences to a given matrix $A$, $\min_{\textrm{rank-}k~B}\|A-B\|_1$, or more generally finding a rank-$k$ matrix $B$ which minimizes the sum of $p$-th powers of absolute values of differences, $\min_{\textrm{rank-}k~B}\|A-B\|_p^p$. Many of these algorithms are linear time columns subset selection algorithms, returning a subset of ${\rm{poly}}(k\log n)$ columns whose cost is no more than a ${\rm{poly}}(k)$ factor larger than the cost of the best rank-$k$ matrix. #R##N#The above error measures are special cases of the following general entrywise low rank approximation problem: given an arbitrary function $g:\mathbb{R}\rightarrow\mathbb{R}_{\geq0}$, find a rank-$k$ matrix $B$ which minimizes $\|A-B\|_g=\sum_{i,j}g(A_{i,j}-B_{i,j})$. A natural question is which functions $g$ admit efficient approximation algorithms? Indeed, this is a central question of recent work studying generalized low rank models. We give approximation algorithms for every function $g$ which is approximately monotone and satisfies an approximate triangle inequality, and we show both of these conditions are necessary. Further, our algorithm is efficient if the function $g$ admits an efficient approximate regression algorithm. Our algorithm handles functions which are not even scale-invariant, such as the Huber loss function. #R##N#Our algorithms have ${\rm{poly}}(k)$-approximation ratio in general. We further improve the approximation ratio to (1+$\epsilon$) for $\ell_1$ loss when the entries of the error matrix are i.i.d. random variables drawn from a distribution $\mu$ of which (1+$\gamma$) moment exists, for an arbitrary small constant $\gamma>0$. We also show our moment assumption is necessary.

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"
    - "sublinear time low rank approximation of distance matrices"
    - "relative error tensor low rank approximation"

Connected through hyperedge HE-3 (induced by source paper "optimal analysis of subset selection based l_p low rank approximation"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "relative error tensor low rank approximation"
    - "how to escape saddle points efficiently"
    - "fast algorithms for robust pca via gradient descent"
    - "low rank approximation of binary matrices column subset selection and generalizations"
    - "optimal cur matrix decompositions"
    - "near optimal column based matrix reconstruction"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-4 (induced by source paper "on mean estimation for general norms with statistical queries"):
  with members:
    - "efficient algorithms and lower bounds for robust linear regression"
    - "subspace embedding and linear regression with orlicz norm"
    - "on the complexity of learning neural networks"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "approximate near neighbors for general symmetric norms"
    - "a general characterization of the statistical query complexity"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "streaming symmetric norms via measure concentration"

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)