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: "beyond highway dimension small distance labels using tree skeletons"
  Abstract: The goal of a hub-based distance labeling scheme for a network G = (V, E) is to assign a small subset S(u) $\subseteq$ V to each node u $\in$ V, in such a way that for any pair of nodes u, v, the intersection of hub sets S(u) $\cap$ S(v) contains a node on the shortest uv-path. The existence of small hub sets, and consequently efficient shortest path processing algorithms, for road networks is an empirical observation. A theoretical explanation for this phenomenon was proposed by Abraham et al. (SODA 2010) through a network parameter they called highway dimension, which captures the size of a hitting set for a collection of shortest paths of length at least r intersecting a given ball of radius 2r. In this work, we revisit this explanation, introducing a more tractable (and directly comparable) parameter based solely on the structure of shortest-path spanning trees, which we call skeleton dimension. We show that skeleton dimension admits an intuitive definition for both directed and undirected graphs, provides a way of computing labels more efficiently than by using highway dimension, and leads to comparable or stronger theoretical bounds on hub set size.

Connected through hyperedge HE-1 (induced by source paper "travelling on graphs with small highway dimension"):
  with members:
    - "hierarchy of transportation network parameters and hardness results"
    - "the parameterized hardness of the k center problem in transportation networks"
    - "a 1 varepsilon embedding of low highway dimension graphs into bounded treewidth graphs"
    - "structural parameters tight bounds and approximation for k r center"
    - "solving generalized maximum weight connected subgraph problem for network enrichment analysis"
    - "fixed parameter approximations for k center problems in low highway dimension graphs"
    - "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme"
    - "efficient activity detection in untrimmed video with max subgraph search"

Connected through hyperedge HE-2 (induced by source paper "hierarchy of transportation network parameters and hardness results"):
  with members:
    - "the parameterized hardness of the k center problem in transportation networks"
    - "a 1 varepsilon embedding of low highway dimension graphs into bounded treewidth graphs"

Connected through hyperedge HE-3 (induced by source paper "exploiting hopsets improved distance oracles for graphs of constant highway dimension and beyond"):
  with members:
    - "linear size hopsets with small hopbound and distributed routing with low memory"
    - "fast and compact exact distance oracle for planar graphs"
    - "better tradeoffs for exact distance oracles in planar graphs"
    - "algorithmic and hardness results for the hub labeling problem"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "hopsets with constant hopbound and applications to approximate shortest paths"
    - "sublinear space distance labeling using hubs"
    - "sublinear distance labeling"

Connected through hyperedge HE-4 (induced by source paper "exact distance oracles using hopsets"):
  with members:
    - "linear size hopsets with small hopbound and distributed routing with low memory"
    - "fast and compact exact distance oracle for planar graphs"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "hopsets with constant hopbound and applications to approximate shortest paths"
    - "sublinear space distance labeling using hubs"
    - "transitive closure spanners"

Connected through hyperedge HE-5 (induced by source paper "a 1 varepsilon embedding of low highway dimension graphs into bounded treewidth graphs"):
  with members:
    - "fixed parameter approximations for k center problems in low highway dimension graphs"
    - "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme"
    - "cops robbers and threatening skeletons padded decomposition for minor free graphs"
    - "lower bounds for embedding into distributions over excluded minor graph families"

Connected through hyperedge HE-6 (induced by source paper "fixed parameter approximations for k center problems in low highway dimension graphs"):
  with members:
    - "the parameterized hardness of the k center problem in transportation networks"
    - "a 1 varepsilon embedding of low highway dimension graphs into bounded treewidth graphs"
    - "structural parameters tight bounds and approximation for k r center"
    - "polynomial time approximation schemes for k center and bounded capacity vehicle routing in metrics with bounded highway dimension"
    - "optimal parameterized algorithms for planar facility location problems using voronoi diagrams"
    - "parameterized approximation schemes using graph widths"

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)