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: "strong coresets for k median and subspace approximation goodbye dimension"
  Abstract: We obtain the first strong coresets for the $k$-median and subspace approximation problems with sum of distances objective function, on $n$ points in $d$ dimensions, with a number of weighted points that is independent of both $n$ and $d$; namely, our coresets have size $\text{poly}(k/\epsilon)$. A strong coreset $(1+\epsilon)$-approximates the cost function for all possible sets of centers simultaneously. We also give efficient $\text{nnz}(A) + (n+d)\text{poly}(k/\epsilon) + \exp(\text{poly}(k/\epsilon))$ time algorithms for computing these coresets. #R##N#We obtain the result by introducing a new dimensionality reduction technique for coresets that significantly generalizes an earlier result of Feldman, Sohler and Schmidt \cite{FSS13} for squared Euclidean distances to sums of $p$-th powers of Euclidean distances for constant $p\ge1$.

Connected through hyperedge HE-1 (induced by source paper "coresets for clustering in graphs of bounded treewidth"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "coresets for ordered weighted clustering"
    - "k means clustering of lines for big data"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "coresets and sketches"
    - "new frameworks for offline and streaming coreset constructions"

Connected through hyperedge HE-2 (induced by source paper "rk means fast clustering for relational data"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "on functional aggregate queries with additive inequalities"
    - "coresets for k means and k median clustering and their applications"
    - "worst case optimal join algorithms techniques results and open problems"
    - "constraint solving via fractional edge covers"
    - "clustering high dimensional dynamic data streams"
    - "in database learning with sparse tensors"
    - "juggling functions inside a database"

Connected through hyperedge HE-3 (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"
    - "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"
    - "on the sensitivity of shape fitting problems"

Connected through hyperedge HE-4 (induced by source paper "random projections and sampling algorithms for clustering of high dimensional polygonal curves"):
  with members:
    - "on the hardness of computing an average curve"
    - "the one way communication complexity of dynamic time warping distance"
    - "walking the dog fast in practice algorithm engineering of the fr echet distance"
    - "beyond worst case analysis"
    - "on coresets for logistic regression"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "clustering is difficult only when it does not matter"

Connected through hyperedge HE-5 (induced by source paper "accurate mapreduce algorithms for k median and k means in general metric spaces"):
  with members:
    - "coresets and sketches"
    - "a constant factor bi criteria approximation guarantee for k means"
    - "space round tradeoffs for mapreduce computations"
    - "fast clustering using mapreduce"
    - "a unified framework for approximating and clustering data"
    - "simpler analyses of local search algorithms for facility location"

Connected through hyperedge HE-6 (induced by source paper "nearly optimal dynamic k means clustering for high dimensional data"):
  with members:
    - "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"
    - "low rank approximation with entrywise ell_1 norm error"
    - "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-7 (induced by source paper "performance of johnson lindenstrauss transform for k means and k medians clustering"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "optimality of the johnson lindenstrauss lemma"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "deterministic feature selection for k means clustering"
    - "new constructions of rip matrices with fast multiplication and fewer rows"
    - "randomized dimensionality reduction for k means clustering"
    - "a sparse johnson lindenstrauss transform"
    - "random projections for k means clustering"

Connected through hyperedge HE-8 (induced by source paper "near optimal sample complexity bounds for robust learning of gaussians mixtures via compression schemes"):
  with members:
    - "sample efficient learning of mixtures"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "learning multivariate log concave distributions"
    - "sample compression schemes for vc classes"
    - "near optimal sample estimators for spherical gaussian mixtures"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"

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)