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: "ba net dense bundle adjustment network"
  Abstract: This paper introduces a network architecture to solve the structure-from-motion (SfM) problem via feature-metric bundle adjustment (BA), which explicitly enforces multi-view geometry constraints in the form of feature-metric error. The whole pipeline is differentiable so that the network can learn suitable features that make the BA problem more tractable. Furthermore, this work introduces a novel depth parameterization to recover dense per-pixel depth. The network first generates several basis depth maps according to the input image and optimizes the final depth as a linear combination of these basis depth maps via feature-metric BA. The basis depth maps generator is also learned via end-to-end training. The whole system nicely combines domain knowledge (i.e. hard-coded multi-view geometry constraints) and deep learning (i.e. feature learning and basis depth maps learning) to address the challenging dense SfM problem. Experiments on large scale real data prove the success of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning of camera pose with compositional re estimation"):
  with members:
    - "on the convergence of adam and beyond"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "megadepth learning single view depth prediction from internet photos"

Connected through hyperedge HE-2 (induced by source paper "cam convs camera aware multi scale convolutions for single view depth"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deeptam deep tracking and mapping"
    - "deep attention based classification network for robust depth prediction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "deepmvs learning multi view stereopsis"

Connected through hyperedge HE-3 (induced by source paper "self supervised learning of depth and ego motion with differentiable bundle adjustment"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "geometry meets semantics for semi supervised monocular depth estimation"

Connected through hyperedge HE-4 (induced by source paper "depth completion via deep basis fitting"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "depth coefficients for depth completion"
    - "dense depth posterior ddp from single image and sparse range"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "voiced depth completion from inertial odometry and vision"
    - "noise aware unsupervised deep lidar stereo fusion"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"

Connected through hyperedge HE-5 (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"
    - "3d pose regression using convolutional neural networks"
    - "superpoint self supervised interest point detection and description"

Connected through hyperedge HE-6 (induced by source paper "beyond photometric loss for self supervised ego motion estimation"):
  with members:
    - "matchable image retrieval by learning from surface reconstruction"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"

Connected through hyperedge HE-7 (induced by source paper "deepsfm structure from motion via deep bundle adjustment"):
  with members:
    - "dpsnet end to end deep plane sweep stereo"
    - "deep ordinal regression network for monocular depth estimation"
    - "deepv2d video to depth with differentiable structure from motion"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "point based multi view stereo network"):
  with members:
    - "dpsnet end to end deep plane sweep stereo"
    - "deepmvs learning multi view stereopsis"
    - "dynamic graph cnn for learning on point clouds"
    - "pu net point cloud upsampling network"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "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"

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)