human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "cube padding for weakly supervised saliency prediction in 360 deg videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spherical image generation from a single normal field of view image by considering scene symmetry")
  Member "object detection in equirectangular panorama":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fddb 360 face detection in 360 degree fisheye images")
  Member "learning spherical convolution for fast features from 360 deg imagery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "silhonet fisheye adaptation of a roi based object pose estimation network to monocular fisheye images")
  Member "deep 360 pilot learning a deep agent for piloting through 360 deg sports video":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "360 o surface regression with a hyper sphere loss")
  Member "salnet360 saliency maps for omni directional images with cnn":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reinforcement learning for the manipulation of eye tracking data")
  Member "joint 2d 3d semantic data for indoor scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "pano2vid automatic cinematography for watching 360 circ videos":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "visual question answering on 360 deg images")
  Member "you only look once unified real time object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

Question: Please tell me which class the source paper 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)