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: "measured descent a new embedding method for finite metrics"
  Abstract: We devise a new embedding technique, which we call measured descent, based on decomposing a metric space locally, at varying speeds, according to the density of some probability measure. This provides a refined and unified framework for the two primary methods of constructing Frechet embeddings for finite metrics, due to Bourgain (1985) and Rao (1999). We prove that any n-point metric space (X, d) embeds in Hilbert space with distortion#N##N#                  #N#                    #N#                  #N#                  $$O{\left( {{\sqrt {\alpha _{X} \cdot \log n} }} \right)},$$#N#                 where α#N#                  X#N#                 is a geometric estimate on the decomposability of X. As an immediate corollary, we obtain an#N##N#                  #N#                    #N#                  #N#                  $$O{\left( {{\sqrt {(\log \lambda _{X} )\log n} }} \right)}$$#N#                 distortion embedding, where λ#N#                  X#N#                 is the doubling constant of X. Since λ#N#                  X#N#                 ≤ n, this result recovers Bourgain’s theorem, but when the metric X is, in a sense, “low-dimensional,” improved bounds are achieved. Our embeddings are volume-respecting for subsets of arbitrary size. One consequence is the existence of (k, O(log n)) volume-respecting embeddings for all 1 ≤ k ≤ n, which is the best possible, and answers positively a question posed by U. Feige. Our techniques are also used to answer positively a question of Y. Rabinovich, showing that any weighted n-point planar graph embeds in#N##N#                  #N#                    #N#                  #N#                  $${\ell }^{{O(\log n)}}_{\infty } $$#N#                 with O(1) distortion. The O(log n) bound on the dimension is optimal, and improves upon the previously known bound of O((log n)2).

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"
    - "metric decompositions of path separable graphs"
    - "space efficient path reporting approximate distance oracles"

Connected through hyperedge HE-3 (induced by source paper "labelings vs embeddings on distributed representations of distances"):
  with members:
    - "lossless prioritized embeddings"
    - "almost optimal distance oracles for planar graphs"
    - "nonlinear dimension reduction via outer bi lipschitz extensions"
    - "optimal terminal dimensionality reduction in euclidean space"
    - "metric embedding via shortest path decompositions"
    - "ramsey spanning trees and their applications"
    - "on notions of distortion and an almost minimum spanning tree with constant average distortion"
    - "prioritized metric structures and embedding"

Connected through hyperedge HE-4 (induced by source paper "covering metric spaces by few trees"):
  with members:
    - "light spanners for high dimensional norms via stochastic decompositions"
    - "ramsey spanning trees and their applications"
    - "a new efficient construction on probabilistic tree embeddings"
    - "a linear size logarithmic stretch path reporting distance oracle for general graphs"
    - "a constructive proof of the general lovasz local lemma"
    - "ramsey partitions and proximity data structures"
    - "fast construction of nets in low dimensional metrics and their applications"
    - "embedding metrics into ultrametrics and graphs into spanning trees with constant average distortion"

Connected through hyperedge HE-5 (induced by source paper "a face cover perspective to ell_1 embeddings of planar graphs"):
  with members:
    - "flow cut gaps and face covers in planar graphs"
    - "nearly eth tight algorithms for planar steiner tree with terminals on few faces"
    - "an exponential lower bound for cut sparsifiers in planar graphs"
    - "metric embedding via shortest path decompositions"
    - "refined vertex sparsifiers of planar graphs"
    - "on notions of distortion and an almost minimum spanning tree with constant average distortion"
    - "vertex sparsifiers new results from old techniques"
    - "cops robbers and threatening skeletons padded decomposition for minor free graphs"

Connected through hyperedge HE-6 (induced by source paper "metric embedding via shortest path decompositions"):
  with members:
    - "metric decompositions of path separable graphs"
    - "cops robbers and threatening skeletons padded decomposition for minor free graphs"
    - "sparsest cut on bounded treewidth graphs algorithms and hardness results"

Connected through hyperedge HE-7 (induced by source paper "quasimetric embeddings and their applications"):
  with members:
    - "randomly removing g handles at once"
    - "optimal stochastic planarization"
    - "nearly tight low stretch spanning trees"

Connected through hyperedge HE-8 (induced by source paper "metric decompositions of path separable graphs"):
  with members:
    - "vertex sparsifiers new results from old techniques"
    - "cops robbers and threatening skeletons padded decomposition for minor free graphs"
    - "cutting corners cheaply or how to remove steiner points"
    - "ramsey partitions and proximity data structures"

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)