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: "cube padding for weakly supervised saliency prediction in 360 deg videos"
  Abstract: Automatic saliency prediction in 360{\deg} videos is critical for viewpoint guidance applications (e.g., Facebook 360 Guide). We propose a spatial-temporal network which is (1) weakly-supervised trained and (2) tailor-made for 360{\deg} viewing sphere. Note that most existing methods are less scalable since they rely on annotated saliency map for training. Most importantly, they convert 360{\deg} sphere to 2D images (e.g., a single equirectangular image or multiple separate Normal Field-of-View (NFoV) images) which introduces distortion and image boundaries. In contrast, we propose a simple and effective Cube Padding (CP) technique as follows. Firstly, we render the 360{\deg} view on six faces of a cube using perspective projection. Thus, it introduces very little distortion. Then, we concatenate all six faces while utilizing the connectivity between faces on the cube for image padding (i.e., Cube Padding) in convolution, pooling, convolutional LSTM layers. In this way, CP introduces no image boundary while being applicable to almost all Convolutional Neural Network (CNN) structures. To evaluate our method, we propose Wild-360, a new 360{\deg} video saliency dataset, containing challenging videos with saliency heatmap annotations. In experiments, our method outperforms baseline methods in both speed and quality.

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"
    - "spherephd applying cnns on a spherical polyhedron representation of 360 degree images"
    - "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 "spherical view synthesis for self supervised 360 depth estimation"):
  with members:
    - "corners for layout end to end layout recovery from 360 images"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "single view stereo matching"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "learning structure from motion from motion"
    - "sfmlearner learning monocular depth ego motion using meaningful geometric constraints"

Connected through hyperedge HE-3 (induced by source paper "coco gan generation by parts via conditional coordinating"):
  with members:
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "cgans with projection discriminator"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "spectral normalization for generative adversarial networks"
    - "painting outside the box image outpainting with gans"
    - "escaping from collapsing modes in a constrained space"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-4 (induced by source paper "360 indoor towards learning real world objects in 360 deg indoor equirectangular images"):
  with members:
    - "yolov3 an incremental improvement"
    - "a deep ranking model for spatio temporal highlight detection from a 360 video"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "learning so 3 equivariant representations with spherical cnns"
    - "making 360 circ video watchable in 2d learning videography for click free viewing"
    - "self view grounding given a narrated 360 deg video"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "distortion adaptive salient object detection in 360 circ omnidirectional images"):
  with members:
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "deeply supervised salient object detection with short connections"
    - "reverse attention for salient object detection"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep saliency with encoded low level distance map and high level features"

Connected through hyperedge HE-6 (induced by source paper "360sd net 360 stereo depth estimation with learnable cost volume"):
  with members:
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "sweepnet wide baseline omnidirectional depth estimation"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "dula net a dual projection network for estimating room layouts from a single rgb panorama"
    - "kernel transformer networks for compact spherical convolution"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "pyramid stereo matching network"
    - "minos multimodal indoor simulator for navigation in complex environments"

Connected through hyperedge HE-7 (induced by source paper "360 degree video gaze behaviour a ground truth data set and a classification algorithm for eye movements"):
  with members:
    - "bayesian identification of fixations saccades and smooth pursuits"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "kernel transformer networks for compact spherical convolution"):
  with members:
    - "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"
    - "learning so 3 equivariant representations with spherical cnns"

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)