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: "a multi orientation analysis approach to retinal vessel tracking"
  Abstract: This paper presents a method for retinal vasculature extraction based on biologically inspired multi-orientation analysis. We apply multi-orientation analysis via so-called invertible orientation scores, modeling the cortical columns in the visual system of higher mammals. This allows us to generically deal with many hitherto complex problems inherent to vessel tracking, such as crossings, bifurcations, parallel vessels, vessels of varying widths and vessels with high curvature. Our approach applies tracking in invertible orientation scores via a novel geometrical principle for curve optimization in the Euclidean motion group SE(2). The method runs fully automatically and provides a detailed model of the retinal vasculature, which is crucial as a sound basis for further quantitative analysis of the retina, especially in screening applications.

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"
    - "global minimum for a finsler elastica minimal path approach"
    - "vtrails inferring vessels with geodesic connectivity trees"

Connected through hyperedge HE-2 (induced by source paper "cortical inspired wilson cowan type equations for orientation dependent contrast perception modelling"):
  with members:
    - "highly corrupted image inpainting through hypoelliptic diffusion"
    - "a cortical inspired model for orientation dependent contrast perception a link with wilson cowan equations"
    - "fourier descriptors based on the structure of the human primary visual cortex with applications to object recognition"
    - "a semidiscrete version of the citti petitot sarti model as a plausible model for anthropomorphic image reconstruction and pattern recognition"
    - "the constitution of visual perceptual units in the functional architecture of v1"

Connected through hyperedge HE-3 (induced by source paper "a recursive bayesian approach to describe retinal vasculature geometry"):
  with members:
    - "synthesising wider field images from narrow field retinal video acquired using a low cost direct ophthalmoscope arclight attached to a smartphone"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "edge ridge and blob detection with symmetric molecules"):
  with members:
    - "scale invariant and noise robust interest points with shearlets"
    - "shearlet based detection of flame fronts"

Connected through hyperedge HE-5 (induced by source paper "template matching via densities on the roto translation group"):
  with members:
    - "generalized principal component analysis gpca"
    - "invariant scattering convolution networks"
    - "robustness and regularization of support vector machines"

Connected through hyperedge HE-6 (induced by source paper "a cortical inspired model for orientation dependent contrast perception a link with wilson cowan equations"):
  with members:
    - "a semidiscrete version of the citti petitot sarti model as a plausible model for anthropomorphic image reconstruction and pattern recognition"
    - "the constitution of visual perceptual units in the functional architecture of v1"

Connected through hyperedge HE-7 (induced by source paper "retrieving challenging vessel connections in retinal images by line co occurrence statistics"):
  with members:
    - "analysis of vessel connectivities in retinal images by cortically inspired spectral clustering"
    - "cortically inspired spectral clustering for connectivity analysis in retinal images curvature integration"

Connected through hyperedge HE-8 (induced by source paper "analysis of vessel connectivities in retinal images by cortically inspired spectral clustering"):
  with members:
    - "the constitution of visual perceptual units in the functional architecture of v1"
    - "a tutorial on spectral clustering"

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.CV(Computer Vision and Pattern Recognition)