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: "lifting object detection datasets into 3d"
  Abstract: While data has certainly taken the center stage in computer vision in recent years, it can still be difficult to obtain in certain scenarios. In particular, acquiring ground truth 3D shapes of objects pictured in 2D images remains a challenging feat and this has hampered progress in recognition-based object reconstruction from a single image. Here we propose to bypass previous solutions such as 3D scanning or manual design, that scale poorly, and instead populate object category detection datasets semi-automatically with dense, per-object 3D reconstructions, bootstrapped from:(i) class labels, (ii) ground truth figure-ground segmentations and (iii) a small set of keypoint annotations. Our proposed algorithm first estimates camera viewpoint using rigid structure-from-motion and then reconstructs object shapes by optimizing over visual hull proposals guided by loose within-class shape similarity assumptions. The visual hull sampling process attempts to intersect an object's projection cone with the cones of minimal subsets of other similar objects among those pictured from certain vantage points. We show that our method is able to produce convincing per-object 3D reconstructions and to accurately estimate cameras viewpoints on one of the most challenging existing object-category detection datasets, PASCAL VOC. We hope that our results will re-stimulate interest on joint object recognition and 3D reconstruction from a single image.

Connected through hyperedge HE-1 (induced by source paper "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep interpretable non rigid structure from motion"
    - "face alignment in full pose range a 3d total solution"
    - "visual object networks image generation with disentangled 3d representation"
    - "learning category specific mesh reconstruction from image collections"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "3d aware scene manipulation via inverse graphics"
    - "inverserendernet learning single image inverse rendering"

Connected through hyperedge HE-2 (induced by source paper "escaping plato s cave 3d shape from adversarial rendering"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "deep single view 3d object reconstruction with visual hull embedding"
    - "single image tomography 3d volumes from 2d cranial x rays"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "learning representations and generative models for 3d point clouds"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "deeporgannet on the fly reconstruction and visualization of 3d 4d lung models from single view projections by deep deformation network"):
  with members:
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "learning free form deformations for 3d object reconstruction"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "single image tomography 3d volumes from 2d cranial x rays"
    - "image2mesh a learning framework for single image 3d reconstruction"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformnet free form deformation network for 3d shape reconstruction from a single image"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"

Connected through hyperedge HE-4 (induced by source paper "escaping plato s cave using adversarial training 3d shape from unstructured 2d image collections"):
  with members:
    - "deep single view 3d object reconstruction with visual hull embedding"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "single image tomography 3d volumes from 2d cranial x rays"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "learning representations and generative models for 3d point clouds"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "learning 3d object categories by looking around them"):
  with members:
    - "demon depth and motion network for learning monocular stereo"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning a predictable and generative vector representation for objects"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "deeper depth prediction with fully convolutional residual networks"
    - "a large dataset of object scans"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"

Connected through hyperedge HE-6 (induced by source paper "compact model representation for 3d reconstruction"):
  with members:
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "hierarchical surface prediction for 3d object reconstruction"
    - "vconv dae deep volumetric shape learning without object labels"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "unsupervised learning of 3d structure from images"
    - "octnet learning deep 3d representations at high resolutions"
    - "perspective transformer nets learning single view 3d object reconstruction without 3d supervision"
    - "single image 3d interpreter network"

Connected through hyperedge HE-7 (induced by source paper "a point set generation network for 3d object reconstruction from a single image"):
  with members:
    - "stacked hourglass networks for human pose estimation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "unsupervised learning of 3d structure from images"
    - "shapenet an information rich 3d model repository"
    - "conditional generative adversarial nets"
    - "depth map prediction from a single image using a multi scale deep network"
    - "category specific object reconstruction from a single image"

Connected through hyperedge HE-8 (induced by source paper "a simple fast and highly accurate algorithm to recover 3d shape from 2d landmarks on a single image"):
  with members:
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "sparseness meets deepness 3d human pose estimation from monocular video"
    - "3d shape estimation from 2d landmarks a convex relaxation approach"
    - "theano new features and speed improvements"

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)