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: "algorithms and hardness for subspace approximation"
  Abstract: The subspace approximation problem Subspace($k$,$p$) asks for a $k$-dimensional linear subspace that fits a given set of points optimally, where the error for fitting is a generalization of the least squares fit and uses the $\ell_{p}$ norm instead. Most of the previous work on subspace approximation has focused on small or constant $k$ and $p$, using coresets and sampling techniques from computational geometry. #R##N#In this paper, extending another line of work based on convex relaxation and rounding, we give a polynomial time algorithm, \emph{for any $k$ and any $p \geq 2$}, with the approximation guarantee roughly $\gamma_{p} \sqrt{2 - \frac{1}{n-k}}$, where $\gamma_{p}$ is the $p$-th moment of a standard normal random variable N(0,1). We show that the convex relaxation we use has an integrality gap (or "rank gap") of $\gamma_{p} (1 - \epsilon)$, for any constant $\epsilon > 0$. Finally, we show that assuming the Unique Games Conjecture, the subspace approximation problem is hard to approximate within a factor better than $\gamma_{p} (1 - \epsilon)$, for any constant $\epsilon > 0$.

Connected through hyperedge HE-1 (induced by source paper "turning big data into tiny data constant size coresets for k means pca and projective clustering"):
  with members:
    - "coresets for k means and k median clustering and their applications"
    - "clustering high dimensional dynamic data streams"
    - "new frameworks for offline and streaming coreset constructions"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal approximate matrix product in terms of stable rank"
    - "dimensionality reduction for k means clustering and low rank approximation"

Connected through hyperedge HE-2 (induced by source paper "testing matrix rank optimally"):
  with members:
    - "improved algorithms for adaptive compressed sensing"
    - "matrix completion and related problems via strong duality"
    - "embeddings of schatten norms with applications to data streams"
    - "on approximating functions of the singular values in a stream"
    - "the dictionary testing problem"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "sketching as a tool for numerical linear algebra"
    - "relations among some low rank subspace recovery models"

Connected through hyperedge HE-3 (induced by source paper "spectrum approximation beyond fast matrix multiplication algorithms and hardness"):
  with members:
    - "fast estimation of approximate matrix ranks using spectral densities"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "embeddings of schatten norms with applications to data streams"
    - "improved stochastic trace estimation using mutually unbiased bases"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "sketches for matrix norms faster smaller and more general"
    - "solving ridge regression using sketched preconditioned svrg"
    - "faster eigenvector computation via shift and invert preconditioning"

Connected through hyperedge HE-4 (induced by source paper "algorithms for ell_p low rank approximation"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "on low rank approximation of binary matrices"
    - "sketching as a tool for numerical linear algebra"
    - "robust pca via outlier pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "on approximating functions of the singular values in a stream"):
  with members:
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "spectrum estimation from samples"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "taylor polynomial estimator for estimating frequency moments"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "sketching as a tool for numerical linear algebra"
    - "sketching and embedding are equivalent for norms"
    - "streaming sums in sublinear space"

Connected through hyperedge HE-6 (induced by source paper "input sparsity and hardness for robust subspace approximation"):
  with members:
    - "ell_p row sampling by lewis weights"
    - "relative errors for deterministic low rank matrix approximations"
    - "subspace embeddings and ell_p regression using exponential random variables"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "on the sensitivity of shape fitting problems"
    - "low rank approximation and regression in input sparsity time"
    - "simple and deterministic matrix sketching"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"

Connected through hyperedge HE-7 (induced by source paper "single image super resolution via manifold approximation"):
  with members:
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-8 (induced by source paper "fast subspace approximation via greedy least squares"):
  with members:
    - "a unified framework for approximating and clustering 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)