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: "prioritized metric structures and embedding"
  Abstract: Metric data structures (distance oracles, distance labeling schemes, routing schemes) and low-distortion embeddings provide a powerful algorithmic methodology, which has been successfully applied for approximation algorithms \cite{llr}, online algorithms \cite{BBMN11}, distributed algorithms \cite{KKMPT12} and for computing sparsifiers \cite{ST04}. However, this methodology appears to have a limitation: the worst-case performance inherently depends on the cardinality of the metric, and one could not specify in advance which vertices/points should enjoy a better service (i.e., stretch/distortion, label size/dimension) than that given by the worst-case guarantee. #R##N#In this paper we alleviate this limitation by devising a suit of {\em prioritized} metric data structures and embeddings. We show that given a priority ranking $(x_1,x_2,\ldots,x_n)$ of the graph vertices (respectively, metric points) one can devise a metric data structure (respectively, embedding) in which the stretch (resp., distortion) incurred by any pair containing a vertex $x_j$ will depend on the rank $j$ of the vertex. We also show that other important parameters, such as the label size and (in some sense) the dimension, may depend only on $j$. In some of our metric data structures (resp., embeddings) we achieve both prioritized stretch (resp., distortion) and label size (resp., dimension) {\em simultaneously}. The worst-case performance of our metric data structures and embeddings is typically asymptotically no worse than of their non-prioritized counterparts.

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 "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"
    - "distance labeling schemes for trees"

Connected through hyperedge HE-3 (induced by source paper "lossless prioritized embeddings"):
  with members:
    - "near isometric terminal embeddings for doubling metrics"
    - "nonlinear dimension reduction via outer bi lipschitz extensions"
    - "optimal terminal dimensionality reduction in euclidean space"
    - "metric embedding via shortest path decompositions"
    - "efficient algorithms for constructing very sparse spanners and emulators"
    - "on notions of distortion and an almost minimum spanning tree with constant average distortion"
    - "the 4 3 additive spanner exponent is tight"
    - "lower stretch spanning trees"

Connected through hyperedge HE-4 (induced by source paper "nonlinear dimension reduction via outer bi lipschitz extensions"):
  with members:
    - "optimal terminal dimensionality reduction in euclidean space"
    - "optimality of the johnson lindenstrauss lemma"
    - "approximate near neighbors for general symmetric norms"
    - "sketching and embedding are equivalent for norms"
    - "constant factor approximation for balanced cut in the pie model"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "metric extension operators vertex sparsifiers and lipschitz extendability"
    - "ramsey type theorems for metric spaces with applications to online problems"

Connected through hyperedge HE-5 (induced by source paper "near isometric terminal embeddings for doubling metrics"):
  with members:
    - "reachability preservers new extremal bounds and approximation algorithms"
    - "a light metric spanner"
    - "on pairwise spanners"
    - "optimal euclidean spanners really short thin and lanky"
    - "ramsey partitions and proximity data structures"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-6 (induced by source paper "on notions of distortion and an almost minimum spanning tree with constant average distortion"):
  with members:
    - "constructing light spanners deterministically in near linear time"
    - "the greedy spanner is existentially optimal"
    - "nearly tight low stretch spanning trees"
    - "embedding metrics into ultrametrics and graphs into spanning trees with constant average distortion"
    - "lower stretch spanning trees"

Connected through hyperedge HE-7 (induced by source paper "distance labeling schemes for trees"):
  with members:
    - "simpler faster and shorter labels for distances in graphs"
    - "adjacency labeling schemes and induced universal graphs"
    - "near optimal labeling schemes for nearest common ancestors"
    - "succinct representations of permutations and functions"
    - "labeling schemes with queries"

Connected through hyperedge HE-8 (induced by source paper "sublinear distance labeling for sparse graphs"):
  with members:
    - "optimal induced universal graphs and adjacency labeling for trees"
    - "simpler faster and shorter labels for distances in graphs"
    - "adjacency labeling schemes and induced universal graphs"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

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)