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: "global minimum for a finsler elastica minimal path approach"
  Abstract: In this paper, we propose a novel curvature penalized minimal path model via an orientation-lifted Finsler metric and the Euler elastica curve. The original minimal path model computes the globally minimal geodesic by solving an Eikonal partial differential equation (PDE). Essentially, this first-order model is unable to penalize curvature which is related to the path rigidity property in the classical active contour models. To solve this problem, we present an Eikonal PDE-based Finsler elastica minimal path approach to address the curvature-penalized geodesic energy minimization problem. We were successful at adding the curvature penalization to the classical geodesic energy (Caselles et al. in Int J Comput Vis 22(1):61---79, 1997; Cohen and Kimmel in Int J Comput Vis 24(1):57---78, 1997). The basic idea of this work is to interpret the Euler elastica bending energy via a novel Finsler elastica metric that embeds a curvature penalty. This metric is non-Riemannian, anisotropic and asymmetric, and is defined over an orientation-lifted space by adding to the image domain the orientation as an extra space dimension. Based on this orientation lifting, the proposed minimal path model can benefit from both the curvature and orientation of the paths. Thanks to the fast marching method, the global minimum of the curvature-penalized geodesic energy can be computed efficiently. We introduce two anisotropic image data-driven speed functions that are computed by steerable filters. Based on these orientation-dependent speed functions, we can apply the proposed Finsler elastica minimal path model to the applications of closed contour detection, perceptual grouping and tubular structure extraction. Numerical experiments on both synthetic and real images show that these applications of the proposed model indeed obtain promising results.

Connected through hyperedge HE-1 (induced by source paper "trajectory grouping with curvature regularization for tubular structure tracking"):
  with members:
    - "minimal paths for tubular structure segmentation with coherence penalty and adaptive anisotropy"
    - "vtrails inferring vessels with geodesic connectivity trees"
    - "a multi orientation analysis approach to retinal vessel tracking"

Connected through hyperedge HE-2 (induced by source paper "fractional wavelet scattering network and applications"):
  with members:
    - "gland instance segmentation using deep multichannel neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "semantic segmentation of colon glands with deep convolutional neural networks and total variation segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "fully convolutional networks for semantic segmentation"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-3 (induced by source paper "eikonal region based active contours for image segmentation"):
  with members:
    - "fast asymmetric fronts propagation for image segmentation"
    - "minimal paths for tubular structure segmentation with coherence penalty and adaptive anisotropy"

Connected through hyperedge HE-4 (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"
    - "fast asymmetric fronts propagation for image segmentation"

Connected through hyperedge HE-5 (induced by source paper "minimal paths for tubular structure segmentation with coherence penalty and adaptive anisotropy"):
  with members:
    - "fast asymmetric fronts propagation for image segmentation"
    - "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)