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 space distance labeling using hubs"
  Abstract: A distance labeling scheme is an assignment of bit-labels to the vertices of an undirected, unweighted graph such that the distance between any pair of vertices can be decoded solely from their labels. We propose a series of new labeling schemes within the framework of so-called hub labeling (HL, also known as landmark labeling or 2-hop-cover labeling), in which each node $u$ stores its distance to all nodes from an appropriately chosen set of hubs $S(u) \subseteq V$. For a queried pair of nodes $(u,v)$, the length of a shortest $u-v$-path passing through a hub node from $S(u)\cap S(v)$ is then used as an upper bound on the distance between $u$ and $v$. #R##N#We present a hub labeling which allows us to decode exact distances in sparse graphs using labels of size sublinear in the number of nodes. For graphs with at most $n$ nodes and average degree $\Delta$, the tradeoff between label bit size $L$ and query decoding time $T$ for our approach is given by $L = O(n \log \log_\Delta T / \log_\Delta T)$, for any $T \leq n$. Our simple approach is thus the first sublinear-space distance labeling for sparse graphs that simultaneously admits small decoding time (for constant $\Delta$, we can achieve any $T=\omega(1)$ while maintaining $L=o(n)$), and it also provides an improvement in terms of label size with respect to previous slower approaches. #R##N#By using similar techniques, we then present a $2$-additive labeling scheme for general graphs, i.e., one in which the decoder provides a 2-additive-approximation of the distance between any pair of nodes. We achieve almost the same label size-time tradeoff $L = O(n \log^2 \log T / \log T)$, for any $T \leq n$. To our knowledge, this is the first additive scheme with constant absolute error to use labels of sublinear size. The corresponding decoding time is then small (any $T=\omega(1)$ is sufficient).

Connected through hyperedge HE-1 (induced by source paper "shorter labels for routing in trees"):
  with members:
    - "near optimal compression for the planar graph metric"
    - "a note on distance labeling in planar graphs"
    - "optimal distance labeling schemes for trees"
    - "optimal induced universal graphs and adjacency labeling for trees"
    - "simpler faster and shorter labels for distances in graphs"
    - "near optimal adjacency labeling scheme for power law graphs"
    - "distance labeling schemes for trees"
    - "a simple and optimal ancestry labeling scheme for trees"

Connected through hyperedge HE-2 (induced by source paper "hardness of exact distance queries in sparse graphs through hub labeling"):
  with members:
    - "near optimal compression for the planar graph metric"
    - "a note on distance labeling in planar graphs"
    - "linear size distance preservers"
    - "optimal distance labeling schemes for trees"
    - "simpler faster and shorter labels for distances in graphs"
    - "maximum matching in turnstile streams"
    - "sublinear distance labeling"
    - "distance labeling schemes for trees"

Connected through hyperedge HE-3 (induced by source paper "shorter labeling schemes for planar graphs"):
  with members:
    - "planar graphs as l intersection or l contact graphs"
    - "proximity results and faster algorithms for integer programming using the steinitz lemma"
    - "optimal distance labeling schemes for trees"
    - "optimal induced universal graphs and adjacency labeling for trees"
    - "simpler faster and shorter labels for distances in graphs"
    - "new routing techniques and their applications"
    - "near optimal labeling schemes for nearest common ancestors"

Connected through hyperedge HE-4 (induced by source paper "near optimal distance emulator for planar graphs"):
  with members:
    - "near optimal compression for the planar graph metric"
    - "subquadratic algorithms for the diameter and the sum of pairwise distances in planar graphs"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "linear size distance preservers"
    - "graph minors for preserving terminal distances approximately lower and upper bounds"
    - "a linear size logarithmic stretch path reporting distance oracle for general graphs"
    - "submatrix maximum queries in monge matrices are equivalent to predecessor search"
    - "the 4 3 additive spanner exponent is tight"

Connected through hyperedge HE-5 (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"
    - "beyond highway dimension small distance labels using tree skeletons"
    - "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 distance labeling"

Connected through hyperedge HE-6 (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"
    - "beyond highway dimension small distance labels using tree skeletons"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "hopsets with constant hopbound and applications to approximate shortest paths"
    - "transitive closure spanners"

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)