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: "fourier descriptors based on the structure of the human primary visual cortex with applications to object recognition"
  Abstract: In this paper, we propose a supervised object recognition method using new global features and inspired by the model of the human primary visual cortex V1 as the semidiscrete roto-translation group $$SE(2,N) = {\mathbb {Z}}_N\rtimes {\mathbb {R}}^2$$SE(2,N)=ZNźR2. The proposed technique is based on generalized Fourier descriptors on the latter group, which are invariant to natural geometric transformations (rotations, translations). These descriptors are then used to feed an SVM classifier. We have tested our method against the COIL-100 image database and the ORL face database, and compared it with other techniques based on traditional descriptors, global and local. The obtained results have shown that our approach looks extremely efficient and stable to noise, in presence of which it outperforms the other techniques analyzed in the paper.

Connected through hyperedge HE-1 (induced by source paper "a new geodesic based feature for characterization of 3d shapes application to soft tissue organ temporal deformations"):
  with members:
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "learning distributions of shape trajectories from longitudinal datasets a hierarchical model on a manifold of diffeomorphisms"
    - "segmatch segment based place recognition in 3d point clouds"

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"
    - "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"
    - "the constitution of visual perceptual units in the functional architecture of v1"

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)