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: "recognizing weak embeddings of graphs"
  Abstract: We present an efficient algorithm for a problem in the interface between clustering and graph embeddings. An embedding $\varphi:G\rightarrow M$ of a graph $G$ into a 2-manifold $M$ maps the vertices in $V(G)$ to distinct points and the edges in $E(G)$ to interior-disjoint Jordan arcs between the corresponding vertices. In applications in clustering, cartography, and visualization, nearby vertices and edges are often bundled to a common node or arc, due to data compression or low resolution. This raises the computational problem of deciding whether a given map $\varphi:G\rightarrow M$ comes from an embedding. A map $\varphi:G\rightarrow M$ is a weak embedding if it can be perturbed into an embedding $\psi_\varepsilon : G\rightarrow M$ with $\|\varphi-\psi_\varepsilon\| 0$. #R##N#A polynomial-time algorithm for recognizing weak embeddings was recently found by [Fulek and Kyn\v{c}l, 2017], which reduces to solving a system of linear equations over $\mathbb{Z}_2$. It runs in $O(n^{2\omega})\leq O(n^{4.75})$ time, where $\omega\approx 2.373$ is the matrix multiplication exponent and $n$ is the number of vertices and edges of $G$. We improve the running time to $O(n\log n)$. Our algorithm is also conceptually simpler than [Fulek and Kyn\v{c}l, 2017]: We perform a sequence of \emph{local operations} that gradually "untangles" the image $\varphi(G)$ into an embedding $\psi(G)$, or reports that $\varphi$ is not a weak embedding. It generalizes a recent technique developed for the case that $G$ is a cycle and the embedding is a simple polygon [Akitaya et al., 2016], and combines local constraints on the orientation of subgraphs directly, thereby eliminating the need for solving large systems of linear equations.

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"
    - "hanani tutte for approximating maps 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:
    - "hanani tutte for approximating maps 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 "homotopic curve shortening and the affine curve shortening flow"):
  with members:
    - "crossing minimization in perturbed drawings"
    - "grid peeling and the affine curve shortening flow"
    - "peeling the grid"
    - "computing homotopic shortest paths efficiently"

Connected through hyperedge HE-4 (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"
    - "hanani tutte for approximating maps 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-5 (induced by source paper "subexponential time and fpt algorithms for embedded flat clustered planarity"):
  with members:
    - "crossing minimization for 1 page and 2 page drawings of graphs with bounded treewidth"
    - "embedded width a variant of treewidth for plane graphs"
    - "c planarity of embedded cyclic c graphs"
    - "clustered planarity with pipes"
    - "a new perspective on clustered planarity as a combinatorial embedding problem"
    - "advances on testing c planarity of embedded flat clustered graphs"
    - "clustered planarity testing revisited"
    - "relaxing the constraints of clustered planarity"

Connected through hyperedge HE-6 (induced by source paper "crossing minimization in perturbed drawings"):
  with members:
    - "hanani tutte for approximating maps 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-7 (induced by source paper "drawing clustered graphs on disk arrangements"):
  with members:
    - "the art gallery problem is exists mathbb r complete"
    - "a new perspective on clustered planarity as a combinatorial embedding problem"

Connected through hyperedge HE-8 (induced by source paper "hanani tutte for approximating maps of graphs"):
  with members:
    - "bounded embeddings of graphs in the plane"
    - "clustered planarity with pipes"
    - "embedding graphs into embedded graphs"
    - "detecting weakly simple polygons"
    - "powers of tensors and fast matrix multiplication"
    - "toward the hanani tutte theorem for clustered graphs"
    - "clustered planarity testing revisited"
    - "simultaneous pq ordering with applications to constrained embedding problems"

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)