human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "exact distance oracles for planar graphs"):
  Abstract: We present new and improved data structures that answer exact node-to-node distance queries in planar graphs. Such data structures are also known as distance oracles. For any directed planar graph on n nodes with non-negative lengths we obtain the following: #R##N#* Given a desired space allocation $S\in[n\lg\lg n,n^2]$, we show how to construct in $\tilde O(S)$ time a data structure of size $O(S)$ that answers distance queries in $\tilde O(n/\sqrt S)$ time per query. #R##N#As a consequence, we obtain an improvement over the fastest algorithm for k-many distances in planar graphs whenever $k\in[\sqrt n,n)$. #R##N#* We provide a linear-space exact distance oracle for planar graphs with query time $O(n^{1/2+eps})$ for any constant eps>0. This is the first such data structure with provable sublinear query time. #R##N#* For edge lengths at least one, we provide an exact distance oracle of space $\tilde O(n)$ such that for any pair of nodes at distance D the query time is $\tilde O(min {D,\sqrt n})$. Comparable query performance had been observed experimentally but has never been explained theoretically. #R##N#Our data structures are based on the following new tool: given a non-self-crossing cycle C with $c = O(\sqrt n)$ nodes, we can preprocess G in $\tilde O(n)$ time to produce a data structure of size $O(n \lg\lg c)$ that can answer the following queries in $\tilde O(c)$ time: for a query node u, output the distance from u to all the nodes of C. This data structure builds on and extends a related data structure of Klein (SODA'05), which reports distances to the boundary of a face, rather than a cycle. #R##N#The best distance oracles for planar graphs until the current work are due to Cabello (SODA'06), Djidjev (WG'96), and Fakcharoenphol and Rao (FOCS'01). For $\sigma\in(1,4/3)$ and space $S=n^\sigma$, we essentially improve the query time from $n^2/S$ to $\sqrt{n^2/S}$.

Includes members:
  Member "more compact oracles for approximate distances in planar graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "1 epsilon distance oracle for planar labeled graph")
  Member "linear space approximate distance oracles for planar bounded genus and minor free graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "holiest minimum cost paths and flows in surface graphs")
  Member "min st cut of a planar graph in o n loglog n time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster energy maximization for faster maximum flow")
  Member "min st cut oracle for planar graphs with near linear preprocessing time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "minimum cuts in surface graphs")
  Member "improved distance queries in planar graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "almost optimal distance oracles for planar graphs")
  Member "shortest paths in planar graphs with real lengths in o n log 2n log log n time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "level planar drawings with few slopes")
  Member "subgraph isomorphism in planar graphs and related problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on subgraph counting concepts algorithms and applications to network motifs and graphlets")

Question: Please tell me which class the source paper 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)