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 interpreter networks for viewer centered wireframe modeling"
  Abstract: Understanding 3D object structure from a single image is an important but challenging task in computer vision, mostly due to the lack of 3D object annotations to real images. Previous research tackled this problem by either searching for a 3D shape that best explains 2D annotations, or training purely on synthetic data with ground truth 3D information. In this work, we propose 3D INterpreter Networks (3D-INN), an end-to-end trainable framework that sequentially estimates 2D keypoint heatmaps and 3D object skeletons and poses. Our system learns from both 2D-annotated real images and synthetic 3D data. This is made possible mainly by two technical innovations. First, heatmaps of 2D keypoints serve as an intermediate representation to connect real and synthetic data. 3D-INN is trained on real images to estimate 2D keypoint heatmaps from an input image; it then predicts 3D object structure from heatmaps using knowledge learned from synthetic 3D shapes. By doing so, 3D-INN benefits from the variation and abundance of synthetic 3D objects, without suffering from the domain difference between real and synthesized images, often due to imperfect rendering. Second, we propose a Projection Layer, mapping estimated 3D structure back to 2D. During training, it ensures 3D-INN to predict 3D structure whose projection is consistent with the 2D annotations to real images. Experiments show that the proposed system performs well on both 2D keypoint estimation and 3D structure recovery. We also demonstrate that the recovered 3D information has wide vision applications, such as image retrieval.

Connected through hyperedge HE-1 (induced by source paper "semi parametric object synthesis"):
  with members:
    - "visual object networks image generation with disentangled 3d representation"
    - "open3d a modern library for 3d data processing"
    - "a variational u net for conditional appearance and shape generation"
    - "semi parametric image synthesis"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "generative image inpainting with contextual attention"
    - "transformation grounded image generation network for novel 3d view synthesis"

Connected through hyperedge HE-2 (induced by source paper "warp and learn novel views generation for vehicles and other objects"):
  with members:
    - "open3d a modern library for 3d data processing"
    - "semi parametric image synthesis"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "generative image inpainting with contextual attention"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "texturegan controlling deep image synthesis with texture patches"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"

Connected through hyperedge HE-3 (induced by source paper "structured3d a large photo realistic dataset for structured 3d modeling"):
  with members:
    - "floor sp inverse cad for floorplans by sequential room wise shortest path"
    - "3d manhattan room layout reconstruction from a single 360 image"
    - "single image piece wise planar 3d reconstruction via associative embedding"
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "learning to reconstruct 3d manhattan wireframes from a single image"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "learning to adapt structured output space for semantic segmentation"
    - "dula net a dual projection network for estimating room layouts from a single rgb panorama"

Connected through hyperedge HE-4 (induced by source paper "learning to infer user interface attributes from images"):
  with members:
    - "image generation from scene graphs"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "pix2code generating code from a graphical user interface screenshot"
    - "learning to infer graphics programs from hand drawn images"
    - "learning from simulated and unsupervised images through adversarial training"
    - "synthetic data for text localisation in natural images"
    - "deep convolutional inverse graphics network"

Connected through hyperedge HE-5 (induced by source paper "single view object shape reconstruction using deep shape prior and silhouette"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "unsupervised learning of shape and pose with differentiable point clouds"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "learning efficient point cloud generation for dense 3d object reconstruction"

Connected through hyperedge HE-6 (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"
    - "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"
    - "unsupervised domain adaptation for 3d keypoint estimation via view consistency"

Connected through hyperedge HE-7 (induced by source paper "geometry aware recurrent neural networks for active visual recognition"):
  with members:
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning a multi view stereo machine"
    - "a dataset for developing and benchmarking active vision"
    - "unsupervised learning of depth and ego motion from video"
    - "neural map structured memory for deep reinforcement learning"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "marrnet 3d shape reconstruction via 2 5d sketches"
    - "iqa visual question answering in interactive environments"

Connected through hyperedge HE-8 (induced by source paper "unsupervised learning of shape and pose with differentiable point clouds"):
  with members:
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "foldingnet interpretable unsupervised learning on 3d point clouds"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "neural 3d mesh renderer"
    - "learning shape abstractions by assembling volumetric primitives"
    - "a versatile scene model with differentiable visibility applied to generative pose 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)