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: "3d pose regression using convolutional neural networks"
  Abstract: 3D pose estimation is a key component of many important computer vision tasks such as autonomous navigation and 3D scene understanding. Most state-of-the-art approaches to 3D pose estimation solve this problem as a pose-classification problem in which the pose space is discretized into bins and a CNN classifier is used to predict a pose bin. We argue that the 3D pose space is continuous and propose to solve the pose estimation problem in a CNN regression framework with a suitable representation, data augmentation and loss function that captures the geometry of the pose space. Experiments on PASCAL3D+ show that the proposed 3D pose regression approach achieves competitive performance compared to the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "6d object pose regression via supervised learning on point clouds"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "open3d a modern library for 3d data processing"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "occlusion resistant object rotation regression from point cloud segments"
    - "when regression meets manifold learning for object recognition and pose estimation"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "a unified framework for multi view multi class object pose estimation"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-2 (induced by source paper "yoloff you only learn offsets for robust 6dof object pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "open3d a modern library for 3d data processing"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "deep 6dpose recovering 6d object pose from a single rgb image"

Connected through hyperedge HE-3 (induced by source paper "relative visual localization for unmanned aerial systems"):
  with members:
    - "learning less is more 6d camera localization via 3d surface regression"

Connected through hyperedge HE-4 (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"
    - "superpoint self supervised interest point detection and description"

Connected through hyperedge HE-5 (induced by source paper "implicit 3d orientation learning for 6d object detection from rgb images"):
  with members:
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "6d pose estimation using an improved method based on point pair features"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "domain adaptation for visual applications a comprehensive survey"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "towards robust learning based pose estimation of noncooperative spacecraft"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pose estimation for non cooperative rendezvous using neural networks"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "yolov3 an incremental improvement"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "estimating 6d pose from localizing designated surface keypoints"

Connected through hyperedge HE-7 (induced by source paper "deep learning for 2d and 3d rotatable data an overview of methods"):
  with members:
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "rotdcf decomposition of convolutional filters for rotation equivariant deep networks"
    - "learning local rgb to cad correspondences for object pose estimation"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "3d human pose estimation with siamese equivariant embedding"
    - "cubenet equivariance to 3d rotation and translation"

Connected through hyperedge HE-8 (induced by source paper "the rgb d triathlon towards agile visual toolboxes for robots"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "adding new tasks to a single network with weight transformations using binary masks"
    - "taskonomy disentangling task transfer learning"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"

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)