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 "coreset based strategies for robust center type problems"):
  Abstract: Given a dataset $V$ of points from some metric space, the popular $k$-center problem requires to identify a subset of $k$ points (centers) in $V$ minimizing the maximum distance of any point of $V$ from its closest center. The \emph{robust} formulation of the problem features a further parameter $z$ and allows up to $z$ points of $V$ (outliers) to be disregarded when computing the maximum distance from the centers. In this paper, we focus on two important constrained variants of the robust $k$-center problem, namely, the Robust Matroid Center (RMC) problem, where the set of returned centers are constrained to be an independent set of a matroid of rank $k$ built on $V$, and the Robust Knapsack Center (RKC) problem, where each element $i\in V$ is given a positive weight $w_i 0$, the algorithms return solutions featuring a $(3+\epsilon)$-approximation ratio, which is a mere additive term $\epsilon$ away from the 3-approximations achievable by the best known polynomial-time sequential algorithms for the two problems. Moreover, the algorithms obliviously adapt to the intrinsic complexity of the dataset, captured by its doubling dimension $D$. For wide ranges of the parameters $k,z,\epsilon, D$, we obtain a sequential algorithm with running time linear in $|V|$, and MapReduce/Streaming algorithms with few rounds/passes and substantially sublinear local/working memory.

Includes members:
  Member "a general coreset based approach to diversity maximization under matroid constraints":
    also connected through other hyperedges:
      (none)
  Member "small space stream summary for matroid center":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how to solve fair k center in massive data models")
  Member "generalized center problems with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fair algorithms for clustering")
  Member "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scalable distributed approximation of internal measures for clustering evaluation")
  Member "a lottery model for center type problems with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximation algorithms for stochastic clustering")
  Member "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a general coreset based approach to diversity maximization under matroid constraints")
  Member "matroid and knapsack center problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dependent randomized rounding for clustering and partition systems with knapsack constraints")
  Member "space round tradeoffs for mapreduce computations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")

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)