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: "spherephd applying cnns on a spherical polyhedron representation of 360 degree images"
  Abstract: Omni-directional cameras have many advantages over conventional cameras in that they have a much wider field-of-view (FOV). Several approaches have been recently proposed to apply convolutional neural networks (CNNs) to omni-directional images to solve classification and detection problems. However, most of them use image representations in the Euclidean space defined by transforming the omni-directional views originally in the non-Euclidean space. This transformation leads to shape distortion due to nonuniform spatial resolving power and loss of continuity. These effects make existing convolution kernels have difficulties in extracting meaningful information. This paper proposes a novel method to resolve the aforementioned problems of applying CNNs to omni-directional images. The proposed method utilizes a spherical polyhedron to represent omni-directional views. This method minimizes the variance of spatial resolving power on the sphere surface, and includes new convolution and pooling methods for the proposed representation. The proposed approach can also be adopted by existing CNN-based methods. The feasibility and efficacy of the proposed method is demonstrated through both classification and detection tasks.

Connected through hyperedge HE-1 (induced by source paper "spherical image generation from a single normal field of view image by considering scene symmetry"):
  with members:
    - "360 panorama synthesis from a sparse set of images with unknown fov"
    - "pluralistic image completion"
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "generative face completion"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "learning so 3 equivariant representations with spherical cnns"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Connected through hyperedge HE-2 (induced by source paper "orientation aware semantic segmentation on icosahedron spheres"):
  with members:
    - "spherical cnns on unstructured grids"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "cubenet equivariance to 3d rotation and translation"
    - "kernel transformer networks for compact spherical convolution"
    - "oriented response networks"
    - "active convolution learning the shape of convolution for image classification"
    - "deformable convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "fddb 360 face detection in 360 degree fisheye images"):
  with members:
    - "dsfd dual shot face detector"
    - "object detection in equirectangular panorama"
    - "can you tell a face from a hevc bitstream"
    - "finding tiny faces"
    - "wider face a face detection benchmark"

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)