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: "the constitution of visual perceptual units in the functional architecture of v1"
  Abstract: Scope of this paper is to consider a mean field neural model which takes into account the functional neurogeometry of the visual cortex modelled as a group of rotations and translations. The model generalizes well known results of Bressloff and Cowan which, in absence of input, accounts for hallucination patterns. The main result of our study consists in showing that in presence of a visual input, the eigenmodes of the linearized operator which become stable represent perceptual units present in the image. The result is strictly related to dimensionality reduction and clustering problems.

Connected through hyperedge HE-1 (induced by source paper "kercnns biologically inspired lateral connections for classification of corrupted images"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "deep learning for classical japanese literature"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "recurrent dropout without memory loss"
    - "curvature integration in a 5d kernel for extracting vessel connections in retinal images"
    - "adam a method for stochastic optimization"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "visualizing and understanding convolutional networks"

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"
    - "a multi orientation analysis approach to retinal vessel tracking"

Connected through hyperedge HE-3 (induced by source paper "neurogeometry of perception isotropic and anisotropic aspects"):
  with members:
    - "local and global gestalt laws a neurally based spectral approach"

Connected through hyperedge HE-4 (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"
    - "a multi orientation analysis approach to retinal vessel tracking"

Connected through hyperedge HE-5 (induced by source paper "curvature integration in a 5d kernel for extracting vessel connections in retinal images"):
  with members:
    - "retrieving challenging vessel connections in retinal images by line co occurrence statistics"
    - "analysis of vessel connectivities in retinal images by cortically inspired spectral clustering"
    - "local and global gestalt laws a neurally based spectral approach"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "analysis of vessel connectivities in retinal images by cortically inspired spectral clustering"):
  with members:
    - "a multi orientation analysis approach to retinal vessel tracking"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "local and global gestalt laws a neurally based spectral approach"):
  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"

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)