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 "c planarity testing of embedded clustered graphs with bounded dual carving width"):
  Abstract: For a clustered graph, i.e, a graph whose vertex set is recursively partitioned into clusters, the C-Planarity Testing problem asks whether it is possible to find a planar embedding of the graph and a representation of each cluster as a region homeomorphic to a closed disk such that 1. the subgraph induced by each cluster is drawn in the interior of the corresponding disk, 2. each edge intersects any disk at most once, and 3. the nesting between clusters is reflected by the representation, i.e., child clusters are properly contained in their parent cluster. The computational complexity of this problem, whose study has been central to the theory of graph visualization since its introduction in 1995 [Qing-Wen Feng, Robert F. Cohen, and Peter Eades. Planarity for clustered graphs. ESA'95], has only been recently settled [Radoslav Fulek and Csaba D. Toth. Atomic Embeddability, Clustered Planarity, and Thickenability. To appear at SODA'20]. Before such a breakthrough, the complexity question was still unsolved even when the graph has a prescribed planar embedding, i.e, for embedded clustered graphs. #R##N#We show that the C-Planarity Testing problem admits a single-exponential single-parameter FPT algorithm for embedded clustered graphs, when parameterized by the carving-width of the dual graph of the input. This is the first FPT algorithm for this long-standing open problem with respect to a single notable graph-width parameter. Moreover, in the general case, the polynomial dependency of our FPT algorithm is smaller than the one of the algorithm by Fulek and Toth. To further strengthen the relevance of this result, we show that the C-Planarity Testing problem retains its computational complexity when parameterized by several other graph-width parameters, which may potentially lead to faster algorithms.

Includes members:
  Member "atomic embeddability clustered planarity and thickenability":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "treetopes and their graphs")
  Member "clustered planarity flat clustered planarity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recognizing weak embeddings of graphs")
  Member "subexponential time and fpt algorithms for embedded flat clustered planarity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "clustered planarity flat clustered planarity")
  Member "embedded width a variant of treewidth for plane graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "subexponential time and fpt algorithms for embedded flat clustered planarity")
  Member "recognizing weak embeddings of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "atomic embeddability clustered planarity and thickenability")
  Member "hanani tutte for approximating maps of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "atomic embeddability clustered planarity and thickenability")
  Member "clustered planarity with pipes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "atomic embeddability clustered planarity and thickenability")
  Member "computing nodetrix representations of clustered graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "simultaneous fpq ordering and hybrid planarity testing")

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)