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: "a fast approximation scheme for low dimensional k means"
  Abstract: We consider the popular $k$-means problem in $d$-dimensional Euclidean space. Recently Friggstad, Rezapour, Salavatipour [FOCS'16] and Cohen-Addad, Klein, Mathieu [FOCS'16] showed that the standard local search algorithm yields a $(1+\epsilon)$-approximation in time $(n \cdot k)^{1/\epsilon^{O(d)}}$, giving the first polynomial-time approximation scheme for the problem in low-dimensional Euclidean space. While local search achieves optimal approximation guarantees, it is not competitive with the state-of-the-art heuristics such as the famous $k$-means++ and $D^2$-sampling algorithms. #R##N#In this paper, we aim at bridging the gap between theory and practice by giving a $(1+\epsilon)$-approximation algorithm for low-dimensional $k$-means running in time $n \cdot k \cdot (\log n)^{(d\epsilon^{-1})^{O(d)}}$, and so matching the running time of the $k$-means++ and $D^2$-sampling heuristics up to polylogarithmic factors. We speed-up the local search approach by making a non-standard use of randomized dissections that allows to find the best local move efficiently using a quite simple dynamic program. We hope that our techniques could help design better local search heuristics for geometric problems. We note that the doubly exponential dependency on $d$ is necessary as $k$-means is APX-hard in dimension $d = \omega(\log n)$.

Connected through hyperedge HE-1 (induced by source paper "k means few more steps yield constant approximation"):
  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 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"
    - "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 "efficient approximation schemes for uniform cost clustering problems in planar graphs"):
  with members:
    - "a polynomial time approximation scheme for facility location on planar graphs"
    - "tight fpt approximations for k median and k means"
    - "quasi polynomial time approximation schemes for packing and covering problems in planar graphs"
    - "new frameworks for offline and streaming coreset constructions"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "optimal parameterized algorithms for planar facility location problems using voronoi diagrams"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "quasi polynomial time approximation scheme for sparse subsets of polygons"

Connected through hyperedge HE-3 (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"
    - "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"
    - "local search yields a ptas for k means in doubling metrics"

Connected through hyperedge HE-4 (induced by source paper "speeding up constrained k means through 2 means"):
  with members:
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "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"
    - "faster algorithms for the constrained k means problem"
    - "minimum bisection is np hard on unit disk graphs"

Connected through hyperedge HE-5 (induced by source paper "approximation schemes for clustering with outliers"):
  with members:
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "the non uniform k center problem"
    - "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"

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)