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 "simple and sharp analysis of k means"):
  Abstract: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from $O(\log\textrm{Var} X)$, where $\textrm{Var} X$ is the variance of the input data set, to $O(\log\textrm{Var} X / \log\log\textrm{Var} X)$, which we show to be tight.

Includes members:
  Member "noisy greedy and not so greedy k means":
    also connected through other hyperedges:
      (none)
  Member "submodular optimization in the mapreduce model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization")
  Member "a constant factor bi criteria approximation guarantee for k means":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "k means few more steps yield constant approximation")
  Member "the power of randomization distributed submodular maximization on massive datasets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regularized submodular maximization at scale")
  Member "improved and simplified inapproximability for k means":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sets clustering")
  Member "the hardness of approximation of euclidean k means":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explainable k means and k medians clustering")
  Member "a new framework for distributed submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regularized submodular maximization at scale")
  Member "a comparative study of efficient initialization methods for the k means clustering algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximating spectral clustering via sampling a review")

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)