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: "local search yields a ptas for k means in doubling metrics"
  Abstract: The most well known and ubiquitous clustering problem encountered in nearly every branch of science is undoubtedly $k$-means: given a set of data points and a parameter $k$, select $k$ centres and partition the data points into $k$ clusters around these centres so that the sum of squares of distances of the points to their cluster centre is minimized. Typically these data points lie $\mathbb{R}^d$ for some $d\geq 2$. #R##N#$k$-means and the first algorithms for it were introduced in the 1950's. Since then, hundreds of papers have studied this problem and many algorithms have been proposed for it. The most commonly used algorithm is known as Lloyd-Forgy, which is also referred to as "the" $k$-means algorithm, and various extensions of it often work very well in practice. However, they may produce solutions whose cost is arbitrarily large compared to the optimum solution. Kanungo et al. [2004] analyzed a simple local search heuristic to get a polynomial-time algorithm with approximation ratio $9+\epsilon$ for any fixed $\epsilon>0$ for $k$-means in Euclidean space. #R##N#Finding an algorithm with a better approximation guarantee has remained one of the biggest open questions in this area, in particular whether one can get a true PTAS for fixed dimension Euclidean space. We settle this problem by showing that a simple local search algorithm provides a PTAS for $k$-means in $\mathbb{R}^d$ for any fixed $d$. More precisely, for any error parameter $\epsilon>0$, the local search algorithm that considers swaps of up to $\rho=d^{O(d)}\cdot{\epsilon}^{-O(d/\epsilon)}$ centres at a time finds a solution using exactly $k$ centres whose cost is at most a $(1+\epsilon)$-factor greater than the optimum. #R##N#Finally, we provide the first demonstration that local search yields a PTAS for the uncapacitated facility location problem and $k$-median with non-uniform opening costs in doubling metrics.

Connected through hyperedge HE-1 (induced by source paper "k means few more steps yield constant approximation"):
  with members:
    - "a fast approximation scheme for low dimensional k means"
    - "a constant factor bi criteria approximation guarantee for k means"
    - "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"
    - "on variants of k means clustering"
    - "improved and simplified inapproximability for k means"
    - "the hardness of approximation of euclidean k means"
    - "scalable k means"

Connected through hyperedge HE-2 (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"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "coresets and sketches"

Connected through hyperedge HE-3 (induced by source paper "a constant fpt approximation algorithm for hard capacitated k means"):
  with members:
    - "constant factor fpt approximation for capacitated k median"
    - "greedy bi criteria approximations for k medians and k means"
    - "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"
    - "on variants of k means clustering"
    - "an approximation algorithm for uniform capacitated k median problem with 1 epsilon capacity violation"
    - "improved and simplified inapproximability for k means"
    - "the hardness of approximation of euclidean k means"

Connected through hyperedge HE-4 (induced by source paper "streaming ptas for constrained k means"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "semi supervised algorithms for approximately optimal and accurate clustering"
    - "a unified framework for clustering constrained data without locality property"
    - "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"
    - "faster algorithms for the constrained k means problem"
    - "the hardness of approximation of euclidean k means"
    - "sampling in space restricted settings"

Connected through hyperedge HE-5 (induced by source paper "noisy greedy and not so greedy k means"):
  with members:
    - "a constant factor bi criteria approximation guarantee for k means"
    - "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"
    - "improved and simplified inapproximability for k means"
    - "the hardness of approximation of euclidean k means"
    - "a comparative study of efficient initialization methods for the k means clustering algorithm"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-6 (induced by source paper "a constant parameterized approximation for hard capacitated k means"):
  with members:
    - "constant factor fpt approximation for capacitated k median"
    - "greedy bi criteria approximations for k medians and k means"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "an approximation algorithm for uniform capacitated k median problem with 1 epsilon capacity violation"
    - "the hardness of approximation of euclidean k means"
    - "on uniform capacitated k median beyond the natural lp relaxation"

Connected through hyperedge HE-7 (induced by source paper "coresets for k l clustering under the frechet distance"):
  with members:
    - "approximating k ell center clustering for curves"
    - "greedy bi criteria approximations for k medians and k means"
    - "technical report of participation in higgs boson machine learning challenge"

Connected through hyperedge HE-8 (induced by source paper "near linear time approximation schemes for clustering in doubling metrics"):
  with members:
    - "travelling on graphs with small highway dimension"
    - "the parameterized hardness of the k center problem in transportation networks"
    - "approximation schemes for capacitated clustering in doubling metrics"
    - "a fast approximation scheme for low dimensional k means"
    - "approximation schemes for clustering with outliers"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "a ptas for the steiner forest problem in doubling metrics"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"

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)