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 "faster shortest paths in dense distance graphs with applications"):
  Abstract: We show how to combine two techniques for efficiently computing shortest paths in directed planar graphs. The first is the linear-time shortest-path algorithm of Henzinger, Klein, Subramanian, and Rao [STOC'94]. The second is Fakcharoenphol and Rao's algorithm [FOCS'01] for emulating Dijkstra's algorithm on the dense distance graph (DDG). A DDG is defined for a decomposition of a planar graph $G$ into regions of at most $r$ vertices each, for some parameter $r < n$. The vertex set of the DDG is the set of $\Theta(n/\sqrt r)$ vertices of $G$ that belong to more than one region (boundary vertices). The DDG has $\Theta(n)$ arcs, such that distances in the DDG are equal to the distances in $G$. Fakcharoenphol and Rao's implementation of Dijkstra's algorithm on the DDG (nicknamed FR-Dijkstra) runs in $O(n\log(n) r^{-1/2} \log r)$ time, and is a key component in many state-of-the-art planar graph algorithms for shortest paths, minimum cuts, and maximum flows. By combining these two techniques we remove the $\log n$ dependency in the running time of the shortest-path algorithm, making it $O(n r^{-1/2} \log^2r)$. #R##N#This work is part of a research agenda that aims to develop new techniques that would lead to faster, possibly linear-time, algorithms for problems such as minimum-cut, maximum-flow, and shortest paths with negative arc lengths. As immediate applications, we show how to compute maximum flow in directed weighted planar graphs in $O(n \log p)$ time, where $p$ is the minimum number of edges on any path from the source to the sink. We also show how to compute any part of the DDG that corresponds to a region with $r$ vertices and $k$ boundary vertices in $O(r \log k)$ time, which is faster than has been previously known for small values of $k$.

Includes members:
  Member "improved bounds for shortest paths in dense distance graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "min cost flow in unit capacity planar graphs")
  Member "minimum cut of directed planar graphs in o nloglogn time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "minimum cuts in surface graphs")
  Member "computing the girth of a planar graph in linear time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "triangles and girth in disk graphs and transmission graphs")
  Member "improved submatrix maximum queries in monge matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "submatrix maximum queries in monge matrices are equivalent to predecessor search")
  Member "maximum st flow in directed planar graphs via shortest paths":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "boundary to boundary flows in planar graphs")
  Member "structured recursive separator decompositions for planar graphs in linear time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the edge crossings of the greedy spanner")
  Member "multiple source shortest paths in embedded graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "minimum cuts in surface graphs")
  Member "multiple source multiple sink maximum flow in directed planar graphs in near linear time":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "minimum cuts in surface graphs")

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)