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 estimation and 3d model retrieval for objects in the wild"
  Abstract: We propose a scalable, efficient and accurate approach to retrieve 3D models for objects in the wild. Our contribution is twofold. We first present a 3D pose estimation approach for object categories which significantly outperforms the state-of-the-art on Pascal3D+. Second, we use the estimated pose as a prior to retrieve 3D models which accurately represent the geometry of objects in RGB images. For this purpose, we render depth images from 3D models under our predicted pose and match learned image descriptors of RGB images against those of rendered depth images using a CNN-based multi-view metric learning approach. In this way, we are the first to report quantitative results for 3D model retrieval on Pascal3D+, where our method chooses the same models as human annotators for 50% of the validation images on average. In addition, we show that our method, which was trained purely on Pascal3D+, retrieves rich and accurate 3D models from ShapeNet given RGB images of objects in the wild.

Connected through hyperedge HE-1 (induced by source paper "unsupervised deep metric learning via auxiliary rotation loss"):
  with members:
    - "on mutual information maximization for representation learning"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "unsupervised pre training of image features on non curated data"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "unsupervised embedding learning via invariant and spreading instance feature"

Connected through hyperedge HE-2 (induced by source paper "location field descriptors single image 3d model retrieval in the wild"):
  with members:
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "triplet center loss for multi view 3d object retrieval"
    - "3d pose estimation for fine grained object categories"
    - "learning shape priors for single view 3d completion and reconstruction"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "ipose instance aware 6d pose estimation of partly occluded objects"

Connected through hyperedge HE-3 (induced by source paper "pose from shape deep pose estimation for arbitrary 3d objects"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "densereg fully convolutional dense shape regression in the wild"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "matching rgb images to cad models for object pose estimation"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "dynamic graph cnn for learning on point clouds"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"

Connected through hyperedge HE-4 (induced by source paper "estimating 3d motion and forces of person object interactions from monocular video"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "end to end recovery of human shape and pose"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "feature mapping for learning fast and accurate 3d pose inference from synthetic images"
    - "a simple yet effective baseline for 3d human pose estimation"

Connected through hyperedge HE-5 (induced by source paper "multi path learning for object pose estimation across domains"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "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"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "few shot viewpoint estimation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "on first order meta learning algorithms"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "few shot segmentation propagation with guided networks"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "prototypical networks for few shot learning"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"

Connected through hyperedge HE-7 (induced by source paper "gp2c geometric projection parameter consensus for joint 3d pose and focal length estimation in the wild"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d pose estimation for fine grained object categories"
    - "a unified framework for multi view multi class object pose estimation"
    - "ipose instance aware 6d pose estimation of partly occluded objects"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"

Connected through hyperedge HE-8 (induced by source paper "conservative wasserstein training for pose estimation"):
  with members:
    - "unsupervised domain adaptation via calibrating uncertainties"
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "deep verifier networks verification of deep discriminative models with deep generative models"
    - "confidence regularized self training"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "deep directional statistics pose estimation with uncertainty quantification"
    - "click here human localized keypoints as guidance for viewpoint estimation"

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)