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: "learning spherical convolution for fast features from 360 deg imagery"
  Abstract: While 360{\deg} cameras offer tremendous new possibilities in vision, graphics, and augmented reality, the spherical images they produce make core feature extraction non-trivial. Convolutional neural networks (CNNs) trained on images from perspective cameras yield "flat" filters, yet 360{\deg} images cannot be projected to a single plane without significant distortion. A naive solution that repeatedly projects the viewing sphere to all tangent planes is accurate, but much too computationally intensive for real problems. We propose to learn a spherical convolutional network that translates a planar CNN to process 360{\deg} imagery directly in its equirectangular projection. Our approach learns to reproduce the flat filter outputs on 360{\deg} data, sensitive to the varying distortion effects across the viewing sphere. The key benefits are 1) efficient feature extraction for 360{\deg} images and video, and 2) the ability to leverage powerful pre-trained networks researchers have carefully honed (together with massive labeled image training sets) for perspective images. We validate our approach compared to several alternative methods in terms of both raw CNN output accuracy as well as applying a state-of-the-art "flat" object detector to 360{\deg} data. Our method yields the most accurate results while saving orders of magnitude in computation versus the existing exact reprojection solution.

Connected through hyperedge HE-1 (induced by source paper "silhonet fisheye adaptation of a roi based object pose estimation network to monocular fisheye images"):
  with members:
    - "woodscape a multi task multi camera fisheye dataset for autonomous driving"
    - "underwater fish detection with weak multi domain supervision"
    - "datasets for face and object detection in fisheye images"
    - "spherical cnns on unstructured grids"
    - "spherical u net on cortical surfaces methods and applications"
    - "tagslam robust slam with fiducial markers"
    - "convolutions on spherical images"
    - "reprojection r cnn a fast and accurate object detector for 360 images"

Connected through hyperedge HE-2 (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"
    - "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 so 3 equivariant representations with spherical cnns"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Connected through hyperedge HE-3 (induced by source paper "neural similarity learning"):
  with members:
    - "compressive hyperspherical energy minimization"
    - "implicit regularization in deep matrix factorization"
    - "a closer look at few shot classification"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "decoupled networks"
    - "meta learning with latent embedding optimization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"

Connected through hyperedge HE-4 (induced by source paper "fisheyedistancenet self supervised scale aware distance estimation using monocular fisheye camera for autonomous driving"):
  with members:
    - "woodscape a multi task multi camera fisheye dataset for autonomous driving"
    - "3d packing for self supervised monocular depth estimation"
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "rethinking imagenet pre training"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "group normalization"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "artistic style transfer for videos and spherical images"

Connected through hyperedge HE-5 (induced by source paper "360 o surface regression with a hyper sphere loss"):
  with members:
    - "corners for layout end to end layout recovery from 360 images"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "planematch patch coplanarity prediction for robust rgb d reconstruction"
    - "three for one and one for three flow segmentation and surface normals"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "scan2cad learning cad model alignment in rgb d scans"
    - "learning to predict indoor illumination from a single image"

Connected through hyperedge HE-6 (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"
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "spherical cnns on unstructured grids"):
  with members:
    - "deep neural networks motivated by partial differential equations"
    - "dynamic graph cnn for learning on point clouds"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "segcloud semantic segmentation of 3d point clouds"
    - "learning so 3 equivariant representations with spherical cnns"
    - "geometric deep learning going beyond euclidean data"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-8 (induced by source paper "pano popups indoor 3d reconstruction with a plane aware network"):
  with members:
    - "convolutions on spherical images"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "megadepth learning single view depth prediction from internet photos"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "joint prediction of depths normals and surface curvature from rgb images using 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)