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: "near optimal compression for the planar graph metric"
  Abstract: The Planar Graph Metric Compression Problem is to compactly encode the distances among $k$ nodes in a planar graph of size $n$. Two na\"ive solutions are to store the graph using $O(n)$ bits, or to explicitly store the distance matrix with $O(k^2 \log{n})$ bits. The only lower bounds are from the seminal work of Gavoille, Peleg, Prennes, and Raz [SODA'01], who rule out compressions into a polynomially smaller number of bits, for {\em weighted} planar graphs, but leave a large gap for unweighted planar graphs. For example, when $k=\sqrt{n}$, the upper bound is $O(n)$ and their constructions imply an $\Omega(n^{3/4})$ lower bound. This gap is directly related to other major open questions in labelling schemes, dynamic algorithms, and compact routing. #R##N#Our main result is a new compression of the planar graph metric into $\tilde{O}(\min (k^2 , \sqrt{k\cdot n}))$ bits, which is optimal up to log factors. Our data structure breaks an $\Omega(k^2)$ lower bound of Krauthgamer, Nguyen, and Zondiner [SICOMP'14] for compression using minors, and the lower bound of Gavoille et al. for compression of weighted planar graphs. This is an unexpected and decisive proof that weights can make planar graphs inherently more complex. Moreover, we design a new {\em Subset Distance Oracle} for planar graphs with $\tilde O(\sqrt{k\cdot n})$ space, and $\tilde O(n^{3/4})$ query time. #R##N#Our work carries strong messages to related fields. In particular, the famous $O(n^{1/2})$ vs. $\Omega(n^{1/3})$ gap for distance labelling schemes in planar graphs {\em cannot} be resolved with the current lower bound techniques.

Connected through hyperedge HE-1 (induced by source paper "shorter labels for routing in trees"):
  with members:
    - "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"
    - "sublinear space distance labeling using hubs"
    - "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 "planar diameter via metric compression"):
  with members:
    - "exact distance oracles for planar graphs with failing vertices"
    - "minor excluded network families admit fast distributed algorithms"
    - "distributed treewidth computation"
    - "faster distributed shortest path approximations via shortcuts"
    - "constant query time 1 epsilon approximate distance oracle for planar graphs"
    - "fast and compact exact distance oracle for planar graphs"
    - "voronoi diagrams on planar graphs and computing the diameter in deterministic tilde o n 5 3 time"
    - "better tradeoffs for exact distance oracles in planar graphs"

Connected through hyperedge HE-3 (induced by source paper "hardness of exact distance queries in sparse graphs through hub labeling"):
  with members:
    - "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"
    - "sublinear space distance labeling using hubs"
    - "maximum matching in turnstile streams"
    - "sublinear distance labeling"
    - "distance labeling schemes for trees"

Connected through hyperedge HE-4 (induced by source paper "near optimal distance emulator for planar graphs"):
  with members:
    - "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"
    - "sublinear space distance labeling using hubs"
    - "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 "improved guarantees for vertex sparsification in planar graphs"):
  with members:
    - "steiner point removal with distortion o log k"
    - "steiner point removal distant terminals don t really bother"
    - "reachability preservers new extremal bounds and approximation algorithms"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "linear size distance preservers"
    - "graph minors for preserving terminal distances approximately lower and upper bounds"
    - "the 4 3 additive spanner exponent is tight"
    - "routing in undirected graphs with constant congestion"

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)