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 "coresets for clustering in graphs of bounded treewidth"):
  Abstract: We initiate the study of coresets for clustering in graph metrics, i.e., the shortest-path metric of edge-weighted graphs. Such clustering problems (on graph metrics) are essential to data analysis and used for example in road networks and data visualization. Specifically, we consider $(k, z)$-Clustering, where given a metric space $(V, d)$, the goal is to minimize, over all $k$-point center sets $C$, the objective $\sum_{x \in V}{d^z(x, C)}$. This problem is a well-known generalization of both k-Median ($z=1$) and k-Means ($z=2$). A coreset is a compact summary of the data that approximately preserves the clustering objective for every possible center set. Coresets offer significant efficiency improvements in terms of running time, storage, and communication, including in streaming and distributed settings. #R##N#Our main result is a near-linear time construction of a coreset of size $O_{\epsilon, k, z}(\mathrm{tw}(G))$ for $(k, z)$-Clustering in a graph $G$ whose treewidth is $\mathrm{tw}(G)$. The construction is based on the framework of Feldman and Langberg [STOC 2011], and our main technical contribution, as required by this framework, is a uniform bound of $O(\mathrm{tw}(G))$ on the shattering dimension under any point weights. Previously, the only construction applicable to graph metrics, even for $z=1$, was a generic one with size $O_{\epsilon, k}(\log n)$ where $n=|V|$ [Feldman and Langberg, STOC 2011]. #R##N#We complement our construction with an $\Omega_{\epsilon, k}(\mathrm{tw}(G))$ size lower bound, which matches our construction's linear dependence on $\mathrm{tw}(G)$. This further provides the first proof that the $O(\log n)$ factor in the generic upper bound is indeed necessary, and also justifies restricting the graph topology.

Includes members:
  Member "coresets for clustering with fairness constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fair correlation clustering")
  Member "coresets for ordered weighted clustering":
    also connected through other hyperedges:
      (none)
  Member "k means clustering of lines for big data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sets clustering")
  Member "turning big data into tiny data constant size coresets for k means pca and projective clustering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fair clustering with multiple colors")
  Member "strong coresets for k median and subspace approximation goodbye dimension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rk means fast clustering for relational data")
  Member "fair coresets and streaming algorithms for fair k means clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fair correlation clustering")
  Member "varepsilon coresets for clustering with outliers in doubling metrics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "layered sampling for robust optimization problems")
  Member "coresets and sketches":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "layered sampling for robust optimization problems")

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)