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 "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.

Includes members:
  Member "clustered planarity flat clustered planarity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "c planarity testing of embedded clustered graphs with bounded dual carving width")
  Member "hanani tutte for approximating maps of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "c planarity testing of embedded clustered graphs with bounded dual carving width")
  Member "clustered planarity with pipes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "c planarity testing of embedded clustered graphs with bounded dual carving width")
  Member "recognizing weakly simple polygons":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "minimum cuts in surface graphs")
  Member "embedding graphs into embedded graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "clustered planarity flat clustered planarity")
  Member "detecting weakly simple polygons":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "minimum cuts in surface graphs")
  Member "strip planarity testing of embedded planar graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "upward book embeddings of st 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.CG(Computational Geometry)