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: "salnet360 saliency maps for omni directional images with cnn"
  Abstract: The prediction of Visual Attention data from any kind of media is of valuable use to content creators and used to efficiently drive encoding algorithms. With the current trend in the Virtual Reality (VR) field, adapting known techniques to this new kind of media is starting to gain momentum. In this paper, we present an architectural extension to any Convolutional Neural Network (CNN) to fine-tune traditional 2D saliency prediction to Omnidirectional Images (ODIs) in an end-to-end manner. We show that each step in the proposed pipeline works towards making the generated saliency map more accurate with respect to ground truth data.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning for the manipulation of eye tracking data"):
  with members:
    - "differential privacy for eye tracking data"
    - "privaceye privacy preserving head mounted eye tracking using egocentric scene image and eye movement features"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "rainbow combining improvements in deep reinforcement learning"
    - "knock knock who s there membership inference on aggregate location data"
    - "a distributional perspective on reinforcement learning"
    - "noisy networks for exploration"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-2 (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-3 (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-4 (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-5 (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"
    - "spherephd applying cnns on a spherical polyhedron representation of 360 degree images"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "saliency prediction on omnidirectional images with generative adversarial imitation learning"):
  with members:
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "deep visual attention prediction"
    - "bridge the gap between vqa and human behavior on omnidirectional video a large scale dataset and a deep learning model"
    - "saltinet scan path prediction on 360 degree images using saliency volumes"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "a deep multi level network for saliency prediction"

Connected through hyperedge HE-7 (induced by source paper "omnidepth dense depth estimation for indoors spherical panoramas"):
  with members:
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "artistic style transfer for videos and spherical images"
    - "light weight pixel context encoders for image inpainting"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "the adobeindoornav dataset towards deep reinforcement learning based real world indoor robot visual navigation"
    - "learning high dynamic range from outdoor panoramas"
    - "active convolution learning the shape of convolution for image classification"

Connected through hyperedge HE-8 (induced by source paper "cube padding for weakly supervised saliency prediction in 360 deg videos"):
  with members:
    - "a deep ranking model for spatio temporal highlight detection from a 360 video"
    - "end to end saliency mapping via probability distribution prediction"
    - "saltinet scan path prediction on 360 degree images using saliency volumes"
    - "viraliency pooling local virality"
    - "the 2017 davis challenge on video object segmentation"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "semantic driven generation of hyperlapse from 360 circ video"
    - "learning video object segmentation with visual memory"

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)