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 6 dof tracking"
  Abstract: We present a temporal 6-DOF tracking method which leverages deep learning to achieve state-of-the-art performance on challenging datasets of real world capture. Our method is both more accurate and more robust to occlusions than the existing best performing approaches while maintaining real-time performance. To assess its efficacy, we evaluate our approach on several challenging RGBD sequences of real objects in a variety of conditions. Notably, we systematically evaluate robustness to occlusions through a series of sequences where the object to be tracked is increasingly occluded. Finally, our approach is purely data-driven and does not require any hand-designed features: robust tracking is automatically learned from data.

Connected through hyperedge HE-1 (induced by source paper "deepim deep iterative matching for 6d pose estimation"):
  with members:
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep model based 6d pose refinement in rgb"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "geometric loss functions for camera pose regression with deep learning"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"

Connected through hyperedge HE-2 (induced by source paper "a markerless deep learning based 6 degrees of freedom poseestimation for with mobile robots using rgb data"):
  with members:
    - "a 3d deep learning based augmented reality calibration method for robotic environments using depth sensor data"
    - "augmented reality based visualization of navigation data of mobile robots on the microsoft hololens possibilities and limitations"
    - "deep hough voting for 3d object detection in point clouds"
    - "estimating 6d pose from localizing designated surface keypoints"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "real time seamless single shot 6d object pose prediction"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-3 (induced by source paper "depth camera based particle filter for robotic osteotomy navigation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "quaternion kinematics for the error state kalman filter"
    - "noise in structured light stereo depth cameras modeling and its applications"
    - "a new perspective and extension of the gaussian filter"
    - "learning analysis by synthesis for 6d pose estimation in rgb d images"

Connected through hyperedge HE-4 (induced by source paper "a 3d deep learning based augmented reality calibration method for robotic environments using depth sensor data"):
  with members:
    - "augmented reality based visualization of navigation data of mobile robots on the microsoft hololens possibilities and limitations"
    - "deep hough voting for 3d object detection in point clouds"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"

Connected through hyperedge HE-5 (induced by source paper "deep fundamental matrix estimation without correspondences"):
  with members:
    - "vision based real estate price estimation"
    - "superpoint self supervised interest point detection and description"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"
    - "convolutional neural network architecture for geometric matching"
    - "unsupervised deep homography a fast and robust homography estimation model"
    - "toward geometric deep slam"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"
    - "relative camera pose estimation using convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "deep model based 6d pose refinement in rgb"):
  with members:
    - "real time 3d model tracking in color and depth on a single cpu core"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "geometric loss functions for camera pose regression with deep learning"
    - "unsupervised learning of depth and ego motion from video"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "on pre trained image features and synthetic images for deep learning"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"

Connected through hyperedge HE-7 (induced by source paper "learning to estimate indoor lighting from 3d objects"):
  with members:
    - "learning to predict indoor illumination from a single image"
    - "material editing using a physically based rendering network"
    - "matterport3d learning from rgb d data in indoor environments"
    - "learning a predictable and generative vector representation for objects"
    - "deep outdoor illumination estimation"
    - "what is around the camera"
    - "delight net decomposing reflectance maps into specular materials and natural illumination"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "a framework for evaluating 6 dof object trackers"):
  with members:
    - "real time 3d model tracking in color and depth on a single cpu core"
    - "geometric loss functions for camera pose regression with deep learning"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "densely connected convolutional networks"
    - "recovering 6d object pose and predicting next best view in the crowd"
    - "fast and accurate deep network learning by exponential linear units elus"

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)