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: "inferring 3d object pose in rgb d images"
  Abstract: The goal of this work is to replace objects in an RGB-D scene with corresponding 3D models from a library. We approach this problem by first detecting and segmenting object instances in the scene using the approach from Gupta et al. [13]. We use a convolutional neural network (CNN) to predict the pose of the object. This CNN is trained using pixel normals in images containing rendered synthetic objects. When tested on real data, it outperforms alternative algorithms trained on real data. We then use this coarse pose estimate along with the inferred pixel support to align a small number of prototypical models to the data, and place the model that fits the best into the scene. We observe a 48% relative improvement in performance at the task of 3D detection over the current state-of-the-art [33], while being an order of magnitude faster at the same time.

Connected through hyperedge HE-1 (induced by source paper "deep supervision with intermediate concepts"):
  with members:
    - "associative domain adaptation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual networks"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "unsupervised learning of 3d structure from images"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "deep supervision with shape concepts for occlusion aware 3d object parsing"

Connected through hyperedge HE-2 (induced by source paper "learning local rgb to cad correspondences for object pose estimation"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "bop benchmark for 6d object pose estimation"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "3d interpreter networks for viewer centered wireframe modeling"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d pose regression using convolutional neural networks"
    - "seethrough finding chairs in heavily occluded indoor scene images"

Connected through hyperedge HE-3 (induced by source paper "matching rgb images to cad models for object pose estimation"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "bop benchmark for 6d object pose estimation"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d pose regression using convolutional neural networks"
    - "unsupervised domain adaptation for 3d keypoint estimation via view consistency"
    - "zero shot deep domain adaptation"
    - "real time seamless single shot 6d object pose prediction"

Connected through hyperedge HE-4 (induced by source paper "ispa net iterative semantic pose alignment network"):
  with members:
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "3d pose regression using convolutional neural networks"
    - "scnet learning semantic correspondence"
    - "rethinking atrous convolution for semantic image segmentation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "unsupervised learning of 3d structure from images"

Connected through hyperedge HE-5 (induced by source paper "sum sequential scene understanding and manipulation"):
  with members:
    - "perch perception via search for multi object recognition and localization"
    - "learning analysis by synthesis for 6d pose estimation in rgb d images"
    - "caffe convolutional architecture for fast feature embedding"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "deep supervision with shape concepts for occlusion aware 3d object parsing"):
  with members:
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "unsupervised learning of 3d structure from images"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "playing for data ground truth from computer games"
    - "single image 3d interpreter network"

Connected through hyperedge HE-7 (induced by source paper "deep cuboid detection beyond 2d bounding boxes"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deep image homography estimation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "ssd single shot multibox detector"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "recurrent human pose estimation"
    - "single image 3d interpreter network"
    - "human pose estimation via convolutional part heatmap regression"

Connected through hyperedge HE-8 (induced by source paper "learning a predictable and generative vector representation for objects"):
  with members:
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "single image 3d interpreter network"
    - "fpnn field probing neural networks for 3d data"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "deep convolutional inverse graphics network"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)