human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "scattering and sparse partitions and their applications"):
  with members:
    - "lossless prioritized embeddings"
    - "labelings vs embeddings on distributed representations of distances"
    - "on strong diameter padded decompositions"
    - "a face cover perspective to ell_1 embeddings of planar graphs"
    - "flow cut gaps and face covers in planar graphs"
    - "near isometric terminal embeddings for doubling metrics"
    - "light spanners for high dimensional norms via stochastic decompositions"
    - "steiner point removal with distortion o log k"

Connected through hyperedge HE-2 (induced by source paper "on strong diameter padded decompositions"):
  with members:
    - "light spanners for high dimensional norms via stochastic decompositions"
    - "ramsey spanning trees and their applications"
    - "the greedy spanner is existentially optimal"
    - "efficient algorithms for constructing very sparse spanners and emulators"
    - "a linear size logarithmic stretch path reporting distance oracle for general graphs"
    - "a light metric spanner"
    - "space efficient path reporting approximate distance oracles"
    - "vertex sparsifiers new results from old techniques"

Connected through hyperedge HE-3 (induced by source paper "metric embedding via shortest path decompositions"):
  with members:
    - "cops robbers and threatening skeletons padded decomposition for minor free graphs"
    - "sparsest cut on bounded treewidth graphs algorithms and hardness results"
    - "measured descent a new embedding method for finite metrics"

Question: Please tell me which class the center node 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)