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 "metric decompositions of path separable graphs"):
  Abstract: A prominent tool in many problems involving metric spaces is a notion of randomized low-diameter decomposition. Loosely speaking, $\beta$-decomposition refers to a probability distribution over partitions of the metric into sets of low diameter, such that nearby points (parameterized by $\beta>0$) are likely to be "clustered" together. Applying this notion to the shortest-path metric in edge-weighted graphs, it is known that $n$-vertex graphs admit an $O(\ln n)$-padded decomposition (Bartal, 1996), and that excluded-minor graphs admit $O(1)$-padded decomposition (Klein, Plotkin and Rao 1993, Fakcharoenphol and Talwar 2003, Abraham et al. 2014). #R##N#We design decompositions to the family of $p$-path-separable graphs, which was defined by Abraham and Gavoille (2006). and refers to graphs that admit vertex-separators consisting of at most $p$ shortest paths in the graph. #R##N#Our main result is that every $p$-path-separable $n$-vertex graph admits an $O(\ln (p \ln n))$-decomposition, which refines the $O(\ln n)$ bound for general graphs, and provides new bounds for families like bounded-treewidth graphs. Technically, our clustering process differs from previous ones by working in (the shortest-path metric of) carefully chosen subgraphs.

Includes members:
  Member "vertex sparsifiers new results from old techniques":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scattering and sparse partitions and their applications")
  Member "cops robbers and threatening skeletons padded decomposition for minor free graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scattering and sparse partitions and their applications")
  Member "cutting corners cheaply or how to remove steiner points":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vertex sparsifiers new results from old techniques")
  Member "ramsey partitions and proximity data structures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "labelings vs embeddings on distributed representations of distances")
  Member "measured descent a new embedding method for finite metrics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scattering and sparse partitions and their applications")

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)