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 "near linear time approximation schemes for clustering in doubling metrics"):
  Abstract: We consider the classic Facility Location, $k$-Median, and $k$-Means problems in metric spaces of doubling dimension $d$. We give nearly-linear time approximation schemes for each problem. The complexity of our algorithms is $2^{(\log(1/\varepsilon)/\varepsilon)^{O(d^2)}} n \log^4 n + 2^{O(d)} n \log^9 n$, making a significant improvement over the state-of-the-art algorithm which runs in time $n^{(d/\varepsilon)^{O(d)}}$.

Includes members:
  Member "travelling on graphs with small highway dimension":
    also connected through other hyperedges:
      (none)
  Member "the parameterized hardness of the k center problem in transportation networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "grundy coloring friends half graphs bicliques")
  Member "approximation schemes for capacitated clustering in doubling metrics":
    also connected through other hyperedges:
      (none)
  Member "a fast approximation scheme for low dimensional k means":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "k means few more steps yield constant approximation")
  Member "approximation schemes for clustering with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "layered sampling for robust optimization problems")
  Member "constant approximation for k median and k means with outliers via iterative rounding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "layered sampling for robust optimization problems")
  Member "a ptas for the steiner forest problem in doubling metrics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "near linear time approximation schemes for steiner tree and forest in low dimensional spaces")
  Member "better guarantees for k means and euclidean k median by primal dual algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fair correlation 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)