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 compact formula for the derivative of a 3 d rotation in exponential coordinates"
  Abstract: We present a compact formula for the derivative of a 3-D rotation matrix with respect to its exponential coordinates. A geometric interpretation of the resulting expression is provided, as well as its agreement with other less-compact but better-known formulas. To the best of our knowledge, this simpler formula does not appear anywhere in the literature. We hope by providing this more compact expression to alleviate the common pressure to reluctantly resort to alternative representations in various computational applications simply as a means to avoid the complexity of differential analysis in exponential coordinates.

Connected through hyperedge HE-1 (induced by source paper "unsupervised depth completion from visual inertial odometry"):
  with members:
    - "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"
    - "deep depth completion of a single rgb d image"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "propagating confidences through cnns for sparse data regression"
    - "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"

Connected through hyperedge HE-2 (induced by source paper "learning to estimate 3d human pose and shape from a single color image"):
  with members:
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "towards accurate markerless human shape and pose estimation over time"
    - "monoperfcap human performance capture from monocular video"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "compositional human pose regression"

Connected through hyperedge HE-3 (induced by source paper "the alignment of the spheres globally optimal spherical mixture alignment for camera pose estimation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "tangent convolutions for dense prediction in 3d"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "geometric loss functions for camera pose regression with deep learning"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "efficient global point cloud alignment using bayesian nonparametric mixtures"

Connected through hyperedge HE-4 (induced by source paper "deception detection by 2d to 3d face reconstruction from videos"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "unsupervised training for 3d morphable model regression"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "morphable face models an open framework"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-5 (induced by source paper "continuous time visual inertial odometry for event cameras"):
  with members:
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "event based 6 dof camera tracking from photometric depth maps"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "on manifold preintegration for real time visual inertial odometry"
    - "event based 6 dof camera tracking for high speed applications"

Connected through hyperedge HE-6 (induced by source paper "a micro lie theory for state estimation in robotics"):
  with members:
    - "on manifold preintegration for real time visual inertial odometry"
    - "quaternion kinematics for the error state kalman filter"

Connected through hyperedge HE-7 (induced by source paper "3d morphable models as spatial transformer networks"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "what does 2d geometric information really tell us about 3d face shape"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "inversefacenet deep single shot inverse face rendering from a single image"
    - "warped convolutions efficient invariance to spatial transformations"

Connected through hyperedge HE-8 (induced by source paper "what does 2d geometric information really tell us about 3d face shape"):
  with members:
    - "learning to regress 3d face shape and expression from an image without 3d supervision"
    - "densereg fully convolutional dense shape regression in the wild"
    - "unsupervised training for 3d morphable model regression"
    - "convolutional point set representation a convolutional bridge between a densely annotated image and 3d face alignment"
    - "evaluation of dense 3d reconstruction from 2d face images in the wild"
    - "nonlinear 3d face morphable model"
    - "learning dense facial correspondences in unconstrained images"
    - "unrestricted facial geometry reconstruction using image to image translation"

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)