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: "fast asymmetric fronts propagation for image segmentation"
  Abstract: In this paper, we introduce a generalized asymmetric fronts propagation model based on the geodesic distance maps and the Eikonal partial differential equations. One of the key ingredients for the computation of the geodesic distance map is the geodesic metric, which can govern the action of the geodesic distance level set propagation. We consider a Finsler metric with the Randers form, through which the asymmetry and anisotropy enhancements can be taken into account to prevent the fronts leaking problem during the fronts propagation. These enhancements can be derived from the image edge-dependent vector field such as the gradient vector flow. The numerical implementations are carried out by the Finsler variant of the fast marching method, leading to very efficient interactive segmentation schemes. We apply the proposed Finsler fronts propagation model to image segmentation applications. Specifically, the foreground and background segmentation is implemented by the Voronoi index map. In addition, for the application of tubularity segmentation, we exploit the level set lines of the geodesic distance map associated with the proposed Finsler metric providing that a thresholding value is given.

Connected through hyperedge HE-1 (induced by source paper "deep eikonal solvers"):
  with members:
    - "deep sets"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "geodesics in heat a new approach to computing distance based on heat flow"

Connected through hyperedge HE-2 (induced by source paper "eikonal region based active contours for image segmentation"):
  with members:
    - "minimal paths for tubular structure segmentation with coherence penalty and adaptive anisotropy"
    - "global minimum for a finsler elastica minimal path approach"

Connected through hyperedge HE-3 (induced by source paper "from active contours to minimal geodesic paths new solutions to active contours problems by eikonal equations"):
  with members:
    - "eikonal region based active contours for image segmentation"
    - "global minimum for a finsler elastica minimal path approach"

Connected through hyperedge HE-4 (induced by source paper "minimal paths for tubular structure segmentation with coherence penalty and adaptive anisotropy"):
  with members:
    - "global minimum for a finsler elastica minimal path approach"
    - "vtrails inferring vessels with geodesic connectivity trees"
    - "a leaf recognition algorithm for plant classification using probabilistic neural network"

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)