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: "hanani tutte for approximating maps of graphs"
  Abstract: We resolve in the affirmative conjectures of Repovs and A. Skopenkov (1998), and M. Skopenkov (2003) generalizing the classical Hanani-Tutte theorem to the setting of approximating maps of graphs on 2-dimensional surfaces by embeddings. Our proof of this result is constructive and almost immediately implies an efficient algorithm for testing if a given piecewise linear map of a graph in a surface is approximable by an embedding. More precisely, an instance of this problem consists of (i) a graph G whose vertices are partitioned into clusters and whose inter-cluster edges are partitioned into bundles, and (ii) a region R of a 2-dimensional compact surface M given as the union of a set of pairwise disjoint discs corresponding to the clusters and a set of pairwise non-intersecting "pipes" corresponding to the bundles, connecting certain pairs of these discs. We are to decide whether G can be embedded inside M so that the vertices in every cluster are drawn in the corresponding disc, the edges in every bundle pass only through its corresponding pipe, and every edge crosses the boundary of each disc at most once.

Connected through hyperedge HE-1 (induced by source paper "c planarity testing of embedded clustered graphs with bounded dual carving width"):
  with members:
    - "atomic embeddability clustered planarity and thickenability"
    - "clustered planarity flat clustered planarity"
    - "subexponential time and fpt algorithms for embedded flat clustered planarity"
    - "embedded width a variant of treewidth for plane graphs"
    - "recognizing weak embeddings of graphs"
    - "clustered planarity with pipes"
    - "computing nodetrix representations of clustered graphs"
    - "a new perspective on clustered planarity as a combinatorial embedding problem"

Connected through hyperedge HE-2 (induced by source paper "atomic embeddability clustered planarity and thickenability"):
  with members:
    - "recognizing weak embeddings of graphs"
    - "clustered planarity with pipes"
    - "recognizing weakly simple polygons"
    - "detecting weakly simple polygons"
    - "simultaneous embedding edge orderings relative positions cutvertices"
    - "clustered planarity testing revisited"
    - "simultaneous embedding of planar graphs"
    - "simultaneous pq ordering with applications to constrained embedding problems"

Connected through hyperedge HE-3 (induced by source paper "clustered planarity flat clustered planarity"):
  with members:
    - "subexponential time and fpt algorithms for embedded flat clustered planarity"
    - "nodetrix planarity testing with small clusters"
    - "recognizing weak embeddings of graphs"
    - "c planarity of embedded cyclic c graphs"
    - "clustered planarity with pipes"
    - "computing nodetrix representations of clustered graphs"
    - "embedding graphs into embedded graphs"
    - "a new perspective on clustered planarity as a combinatorial embedding problem"

Connected through hyperedge HE-4 (induced by source paper "crossing minimization in perturbed drawings"):
  with members:
    - "recognizing weak embeddings of graphs"
    - "recognizing weakly simple polygons"
    - "detecting weakly simple polygons"
    - "adding one edge to planar graphs makes crossing number and 1 planarity hard"

Connected through hyperedge HE-5 (induced by source paper "recognizing weak embeddings of graphs"):
  with members:
    - "clustered planarity flat clustered planarity"
    - "clustered planarity with pipes"
    - "recognizing weakly simple polygons"
    - "embedding graphs into embedded graphs"
    - "detecting weakly simple polygons"
    - "strip planarity testing of embedded planar graphs"

Connected through hyperedge HE-6 (induced by source paper "clustered planarity with pipes"):
  with members:
    - "subexponential time and fpt algorithms for embedded flat clustered planarity"
    - "recognizing weak embeddings of graphs"
    - "c planarity of embedded cyclic c graphs"
    - "embedding graphs into embedded graphs"
    - "a new perspective on clustered planarity as a combinatorial embedding problem"
    - "detecting weakly simple polygons"
    - "toward the hanani tutte theorem for clustered graphs"
    - "advancements on sefe and partitioned book embedding problems"

Connected through hyperedge HE-7 (induced by source paper "recognizing weakly simple polygons"):
  with members:
    - "embedding graphs into embedded graphs"
    - "detecting weakly simple polygons"
    - "flat foldings of plane graphs with prescribed angles and edge lengths"
    - "when can you fold a map"

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.CG(Computational Geometry)