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: "sublinear distance labeling for sparse graphs"
  Abstract: A distance labeling scheme labels the n nodes of a graph with binary strings such that, given the labels of any two nodes, one can determine the distance in the graph between the two nodes by looking only at the labels. A D-preserving distance labeling scheme only returns precise distances between pairs of nodes that are at distance at least D from each other. In this paper we consider distance labeling schemes for the classical case of unweighted and undirected graphs. We present the first distance labeling scheme of size opnq for sparse graphs (and hence bounded degree graphs). This addresses an open problem by Gavoille et. al. [J. Algo. 2004], hereby separating the complexity from general graphs which require pnq size Moon [Proc. of Glasgow Math. Association 1965]. As an intermediate result we give a Op n D log

Connected through hyperedge HE-1 (induced by source paper "pruning based distance sketches with provable guarantees on random graphs"):
  with members:
    - "an axiomatic and an average case analysis of algorithms and heuristics for metric properties of graphs"
    - "algorithmic and hardness results for the hub labeling problem"
    - "on the complexity of hub labeling"
    - "route planning in transportation networks"
    - "hop doubling label indexing for point to point distance querying on scale free networks"
    - "a scalable generative graph model with community structure"
    - "separating hierarchical and general hub labelings"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

Connected through hyperedge HE-2 (induced by source paper "popular conjectures as a barrier for dynamic planar graph algorithms"):
  with members:
    - "improved bounds for shortest paths in dense distance graphs"
    - "on the hardness of partially dynamic graph problems and connections to diameter"
    - "faster online matrix vector multiplication"
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "simpler faster and shorter labels for distances in graphs"
    - "a faster algorithm for the discrete frechet distance under translation"
    - "fully dynamic matching in bipartite graphs"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "linear size distance preservers"):
  with members:
    - "thorup zwick emulators are universally optimal hopsets"
    - "distance preserving subgraphs of interval graphs"
    - "multiple source dual fault tolerant bfs trees"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "a linear size logarithmic stretch path reporting distance oracle for general graphs"
    - "dual failure resilient bfs structure"
    - "very sparse additive spanners and emulators"
    - "better distance preservers and additive spanners"

Connected through hyperedge HE-4 (induced by source paper "sublinear space distance labeling using hubs"):
  with members:
    - "simpler faster and shorter labels for distances in graphs"
    - "approximate distance oracle with constant query time"
    - "approximate distance oracles with improved query time"
    - "on the hardness of distance oracle for sparse graph"

Connected through hyperedge HE-5 (induced by source paper "even simpler distance labeling for sparse graphs"):
  with members:
    - "simpler faster and shorter labels for distances in graphs"
    - "approximate distance oracle with constant query time"
    - "on the hardness of distance oracle for sparse graph"

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)