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: "unsupervised deep homography a fast and robust homography estimation model"
  Abstract: Homography estimation between multiple aerial images can provide relative pose estimation for collaborative autonomous exploration and monitoring. The usage on a robotic system requires a fast and robust homography estimation algorithm. In this study, we propose an unsupervised learning algorithm that trains a Deep Convolutional Neural Network to estimate planar homographies. We compare the proposed algorithm to traditional feature-based and direct methods, as well as a corresponding supervised learning algorithm. Our empirical results demonstrate that compared to traditional approaches, the unsupervised algorithm achieves faster inference speed, while maintaining comparable or better accuracy and robustness to illumination variation. In addition, on both a synthetic dataset and representative real-world aerial dataset, our unsupervised method has superior adaptability and performance compared to the supervised deep learning method.

Connected through hyperedge HE-1 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects"):
  with members:
    - "learning to calibrate straight lines for fisheye image rectification"
    - "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"
    - "resource allocation for solar powered uav communication systems"
    - "throughput maximization for laser powered uav wireless communication systems"
    - "learning attraction field representation for robust line segment detection"
    - "anisotropic scale junction detection and matching for indoor images"
    - "deepmvs learning multi view stereopsis"
    - "the uavid dataset for video semantic segmentation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "evdodgenet deep dynamic obstacle dodging with event cameras"):
  with members:
    - "event based motion segmentation by motion compensation"
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "event based vision a survey"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "event based moving object detection and tracking"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"

Connected through hyperedge HE-4 (induced by source paper "content aware unsupervised deep homography estimation"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "frame recurrent video inpainting by robust optical flow inference"
    - "semi supervised video salient object detection using pseudo labels"
    - "two phase hair image synthesis by self enhancing generative model"
    - "learning raw image denoising with bayer pattern unification and bayer preserving augmentation"
    - "semi supervised skin detection by network with mutual guidance"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "mganet a robust model for quality enhancement of compressed video"

Connected through hyperedge HE-5 (induced by source paper "semi supervised semantic matching"):
  with members:
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "end to end weakly supervised semantic alignment"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"
    - "convolutional neural network architecture for geometric matching"
    - "image based localization using hourglass networks"
    - "unsupervised learning of depth and ego motion from video"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "scnet learning semantic correspondence"

Connected through hyperedge HE-6 (induced by source paper "copy and paste networks for deep video inpainting"):
  with members:
    - "deep video inpainting"
    - "free form image inpainting with gated convolution"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "generative image inpainting with contextual attention"
    - "the 2017 davis challenge on video object segmentation"
    - "perceptual losses for real time style transfer and super resolution"
    - "deep image homography estimation"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-7 (induced by source paper "stn homography estimate homography parameters directly"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "quasi homography warps in image stitching"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "deep image homography estimation"
    - "sgdr stochastic gradient descent with restarts"
    - "spatial transformer networks"
    - "flownet learning optical flow with convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "deepmeshflow content adaptive mesh deformation for robust image registration"):
  with members:
    - "handheld multi frame super resolution"
    - "content aware unsupervised deep homography estimation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "deep image homography estimation"
    - "spatial transformer networks"
    - "adam a method for stochastic optimization"

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)