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: "a survey of structure from motion"
  Abstract: The structure from motion (SfM) problem in computer vision is the problem of recovering the three-dimensional ($3$D) structure of a stationary scene from a set of projective measurements, represented as a collection of two-dimensional ($2$D) images, via estimation of motion of the cameras corresponding to these images. In essence, SfM involves the three main stages of (1) extraction of features in images (e.g., points of interest, lines, etc.) and matching these features between images, (2) camera motion estimation (e.g., using relative pairwise camera positions estimated from the extracted features), and (3) recovery of the $3$D structure using the estimated motion and features (e.g., by minimizing the so-called reprojection error). This survey mainly focuses on relatively recent developments in the literature pertaining to stages (2) and (3). More specifically, after touching upon the early factorization-based techniques for motion and structure estimation, we provide a detailed account of some of the recent camera location estimation methods in the literature, followed by discussion of notable techniques for $3$D structure recovery. We also cover the basics of the simultaneous localization and mapping (SLAM) problem, which can be viewed as a specific case of the SfM problem. Further, our survey includes a review of the fundamentals of feature extraction and matching (i.e., stage (1) above), various recent methods for handling ambiguities in $3$D scenes, SfM techniques involving relatively uncommon camera models and image features, and popular sources of data and SfM software.

Connected through hyperedge HE-1 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "attention based deep multiple instance learning"
    - "deepmvs learning multi view stereopsis"
    - "attention based pyramid aggregation network for visual place recognition"
    - "deep attentional structured representation learning for visual recognition"
    - "deep sets"
    - "deep imbalanced attribute classification using visual attention aggregation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "active perception and representation for robotic manipulation"):
  with members:
    - "saccader improving accuracy of hard attention models for vision"
    - "model based reinforcement learning for atari"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning actionable representations from visual observations"
    - "visual reinforcement learning with imagined goals"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-4 (induced by source paper "pix2vox context aware 3d reconstruction from single and multi view images"):
  with members:
    - "dense 3d object reconstruction from a single depth view"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "realpoint3d point cloud generation from a single image with complex background"
    - "3d lmnet latent embedding matching for accurate and diverse 3d point cloud reconstruction from a single image"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"

Connected through hyperedge HE-5 (induced by source paper "global visual localization in lidar maps through shared 2d 3d embedding space"):
  with members:
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "2d3d matchnet learning to match keypoints across 2d image and 3d point cloud"
    - "cmrnet camera to lidar map registration"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "dynamic graph cnn for learning on point clouds"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-6 (induced by source paper "robust motion segmentation from pairwise matches"):
  with members:
    - "motion segmentation by exploiting complementary geometric models"
    - "multi class model fitting by energy minimization and mode seeking"
    - "spectral synchronization of multiple views in se 3"
    - "shape interaction matrix revisited and robustified efficient subspace clustering with corrupted and incomplete data"
    - "on constrained spectral clustering and its applications"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-7 (induced by source paper "ground plane based absolute scale estimation for monocular visual odometry"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "learning based image enhancement for visual odometry in challenging hdr environments"
    - "probabilistic global scale estimation for monoslam based on generic object detection"
    - "inertial based scale estimation for structure from motion on mobile devices"
    - "deepvo a deep learning approach for monocular visual odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-8 (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"

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)