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: "deep image homography estimation"
  Abstract: We present a deep convolutional neural network for estimating the relative homography between a pair of images. Our feed-forward network has 10 layers, takes two stacked grayscale images as input, and produces an 8 degree of freedom homography which can be used to map the pixels from the first image to the second. We present two convolutional neural network architectures for HomographyNet: a regression network which directly estimates the real-valued homography parameters, and a classification network which produces a distribution over quantized homographies. We use a 4-point homography parameterization which maps the four corners from one image into the second image. Our networks are trained in an end-to-end fashion using warped MS-COCO images. Our approach works without the need for separate local feature detection and transformation estimation stages. Our deep models are compared to a traditional homography estimator based on ORB features and we highlight the scenarios where HomographyNet outperforms the traditional technique. We also describe a variety of applications powered by deep homography estimation, thus showcasing the flexibility of a deep learning approach.

Connected through hyperedge HE-1 (induced by source paper "consac robust multi model fitting by conditional sample consensus"):
  with members:
    - "neural guided ransac learning where to sample model hypotheses"
    - "progressive x efficient anytime multi model fitting algorithm"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "multi class model fitting by energy minimization and mode seeking"
    - "deep learning for vanishing point detection using an inverse gnomonic projection"

Connected through hyperedge HE-2 (induced by source paper "highres net recursive fusion for multi frame super resolution of satellite imagery"):
  with members:
    - "tackling climate change with machine learning"
    - "learning parallax attention for stereo image super resolution"
    - "frame and feature context video super resolution"
    - "deep learning for image super resolution a survey"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "learning disentangled representations of satellite image time series"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "mapping informal settlements in developing countries with multi resolution multi spectral data"

Connected through hyperedge HE-3 (induced by source paper "drmime differentiable mutual information and matrix exponential for multi resolution image registration"):
  with members:
    - "airlab autograd image registration laboratory"
    - "template matching via densities on the roto translation group"
    - "mine mutual information neural estimation"

Connected through hyperedge HE-4 (induced by source paper "towards robust learning with different label noise distributions"):
  with members:
    - "deep self learning from noisy labels"
    - "are anchor points really indispensable in label noise learning"
    - "accelerating deep learning by focusing on the biggest losers"
    - "pseudo labeling and confirmation bias in deep semi supervised learning"
    - "nlnl negative learning for noisy labels"
    - "unsupervised label noise modeling and loss correction"
    - "using pre training can improve model robustness and uncertainty"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-5 (induced by source paper "ai benchmark all about deep learning on smartphones in 2019"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "on device neural net inference with mobile gpus"
    - "semantic image synthesis with spatially adaptive normalization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "synthetic depth of field with a single camera mobile phone"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "pirm challenge on perceptual image enhancement on smartphones report"
    - "ai benchmark running deep neural networks on android smartphones"

Connected through hyperedge HE-6 (induced by source paper "superglue learning feature matching with graph neural networks"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "dual graph convolutional network for semantic segmentation"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "neural guided ransac learning where to sample model hypotheses"
    - "graph matching networks for learning the similarity of graph structured objects"
    - "deep closest point learning representations for point cloud registration"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "ppfnet global context aware local features for robust 3d point matching"

Connected through hyperedge HE-7 (induced by source paper "optimizing through learned errors for accurate sports field registration"):
  with members:
    - "regnet learning the optimization of direct image to image pose registration"
    - "lf net learning local features from images"
    - "sports camera calibration via synthetic data"
    - "ls net learning to solve nonlinear least squares for monocular stereo"
    - "spectral normalization for generative adversarial networks"
    - "acquisition of localization confidence for accurate object detection"
    - "ba net dense bundle adjustment network"
    - "3d pose regression using convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "contextdesc local descriptor augmentation with cross modality context"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "matchable image retrieval by learning from surface reconstruction"
    - "fix your classifier the marginal value of training the last weight layer"
    - "local descriptors optimized for average precision"
    - "learning and matching multi view descriptors for registration of point clouds"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "repeatability is not enough learning affine regions via discriminability"

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)