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 "an o log 1 5 n log log n approximation algorithm for mean isoperimetry and robust k means"):
  Abstract: Given a weighted graph $G=(V,E)$, and $U\subseteq V$, the normalized cut value for $U$ is defined as the sum of the weights of edges exiting $U$ divided by the number of vertices in $U$. The \textsc{mean isoperimetry problem} for weighted graphs is a generalization of the sparsest cut problem in which, given a parameter $k$ alongside a weighted graph $G=(V,E)$, the objective is to find $k$ disjoint nonempty subsets of $V$ for which the average normalized cut value of the parts is as small as possible. In this paper, we present an $O(\log^{1.5}n \log\log n)$ approximation algorithm for the \textsc{mean isoperimetry problem}. Our approach is based on developing an exact dynamic programming algorithm for this problem on weighted trees. We also show how our algorithm can be utilized to approximately solve a robust version of the $k$-means clustering problem in $\mathbb{R}^d$, in which there can be some ``outlier'' points, lying outside the $k$ parts. For this, the standard $k$-means objective function is modified to take into account the effect of the outliers by adding some penalty function $g$ for them. We show that for a relatively general class of penalty functions $g$, this problem admits an $O(\log^{1.5}n \log\log n)$-approximation algorithm, whose running time is polynomial in $n$, $k$, and $d$.

Includes members:
  Member "approximation schemes for clustering with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "layered sampling for robust optimization problems")
  Member "the power of local search for clustering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "one size fits all effectiveness of local search on structured data")
  Member "better guarantees for k means and euclidean k median by primal dual algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "k means few more steps yield constant approximation")
  Member "the hardness of approximation of euclidean k means":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)