human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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 $d = \omega(\log n)$.

Includes members:
  Member "tight analysis of a multiple swap heuristic for budgeted red blue median":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "one size fits all effectiveness of local search on structured data")
  Member "new frameworks for offline and streaming coreset constructions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sets clustering")
  Member "better guarantees for k means and euclidean k median by primal dual algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fair correlation clustering")
  Member "local search yields a ptas for k means in doubling metrics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "k means few more steps yield constant approximation")
  Member "on variants of k means clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "k means few more steps yield constant approximation")
  Member "optimal parameterized algorithms for planar facility location problems using voronoi diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "grundy coloring friends half graphs bicliques")
  Member "a bi criteria approximation algorithm for k means":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "low rank approximation from communication complexity")
  Member "the hardness of approximation of euclidean k means":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "explainable k means and k medians clustering")

Question: Please tell me which class the source paper 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)