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: "graph based classification of omnidirectional images"
  Abstract: Omnidirectional cameras are widely used in such areas as robotics and virtual reality as they provide a wide field of view. Their images are often processed with classical methods, which might unfortunately lead to non-optimal solutions as these methods are designed for planar images that have different geometrical properties than omnidirectional ones. In this paper we study image classification task by taking into account the specific geometry of omnidirectional cameras with graph-based representations. In particular, we extend deep learning architectures to data on graphs; we propose a principled way of graph construction such that convolutional filters respond similarly for the same pattern on different positions of the image regardless of lens distortions. Our experiments show that the proposed method outperforms current techniques for the omnidirectional image classification problem.

Connected through hyperedge HE-1 (induced by source paper "kernel transformer networks for compact spherical convolution"):
  with members:
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "a deep ranking model for spatio temporal highlight detection from a 360 video"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "active convolution learning the shape of convolution for image classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"

Connected through hyperedge HE-2 (induced by source paper "object detection in equirectangular panorama"):
  with members:
    - "soft nms improving object detection with one line of code"
    - "yolo9000 better faster stronger"
    - "pano2vid automatic cinematography for watching 360 circ videos"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "learning spherical convolution for fast features from 360 deg imagery"):
  with members:
    - "deformable convolutional networks"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "semantic driven generation of hyperlapse from 360 circ video"
    - "convolutional networks for spherical signals"
    - "making 360 circ video watchable in 2d learning videography for click free viewing"
    - "mask r cnn"
    - "convolutional two stream network fusion for video action recognition"
    - "pano2vid automatic cinematography for watching 360 circ videos"

Connected through hyperedge HE-4 (induced by source paper "learning compressible 360 deg video isomers"):
  with members:
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "convolutional networks for spherical signals"
    - "learning convolutional networks for content weighted image compression"
    - "full resolution image compression with recurrent neural networks"
    - "pano2vid automatic cinematography for watching 360 circ videos"
    - "variable rate image compression with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)