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: "virtual view networks for object reconstruction"
  Abstract: All that structure from motion algorithms "see" are sets of 2D points. We show that these impoverished views of the world can be faked for the purpose of reconstructing objects in challenging settings, such as from a single image, or from a few ones far apart, by recognizing the object and getting help from a collection of images of other objects from the same class. We synthesize virtual views by computing geodesics on novel networks connecting objects with similar viewpoints, and introduce techniques to increase the specificity and robustness of factorization-based object reconstruction in this setting. We report accurate object shape reconstruction from a single image on challenging PASCAL VOC data, which suggests that the current domain of applications of rigid structure-from-motion techniques may be significantly extended.

Connected through hyperedge HE-1 (induced by source paper "c3dpo canonical 3d pose networks for non rigid structure from motion"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "deep high resolution representation learning for human pose estimation"
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised adversarial learning of 3d human pose from 2d joint locations"
    - "learning 3d human dynamics from video"
    - "can 3d pose be learned from 2d projections alone"

Connected through hyperedge HE-2 (induced by source paper "multi image semantic matching by mining consistent features"):
  with members:
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "proposal flow semantic correspondences from object proposals"
    - "unsupervised learning of object landmarks by factorized spatial embeddings"
    - "lift learned invariant feature transform"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "multi graph matching via affinity optimization with graduated consistency regularization"
    - "learning dense correspondence via 3d guided cycle consistency"
    - "learning covariant feature detectors"

Connected through hyperedge HE-3 (induced by source paper "3d reconstruction of simple objects from a single view silhouette image"):
  with members:
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "perspective transformer nets learning single view 3d object reconstruction without 3d supervision"
    - "single image 3d interpreter network"
    - "unified depth prediction and intrinsic image decomposition from a single image via joint convolutional neural fields"
    - "multi view 3d models from single images with a convolutional network"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"

Connected through hyperedge HE-4 (induced by source paper "sparse representation for 3d shape estimation a convex relaxation approach"):
  with members:
    - "robust estimation of 3d human poses from a single image"
    - "3d shape estimation from 2d landmarks a convex relaxation approach"
    - "category specific object reconstruction from a single image"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "optimization with sparsity inducing penalties"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-5 (induced by source paper "learning dense correspondence via 3d guided cycle consistency"):
  with members:
    - "deep sliding shapes for amodal 3d object detection in rgb d images"
    - "dense semantic correspondence where every pixel is a classifier"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "spatial transformer networks"
    - "learning image representations tied to ego motion"
    - "deepstereo learning to predict new views from the world s imagery"
    - "learning to see by moving"
    - "unsupervised object discovery and localization in the wild part based matching with bottom up region proposals"

Connected through hyperedge HE-6 (induced by source paper "warpnet weakly supervised matching for single view reconstruction"):
  with members:
    - "spatial transformer networks"
    - "flownet learning optical flow with convolutional networks"
    - "learning to see by moving"
    - "learning to compare image patches via convolutional neural networks"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "return of the devil in the details delving deep into convolutional nets"
    - "viewpoints and keypoints"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-7 (induced by source paper "symmetry aware depth estimation using deep neural networks"):
  with members:
    - "shapenet an information rich 3d model repository"
    - "depth map prediction from a single image using a multi scale deep network"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

Connected through hyperedge HE-8 (induced by source paper "pose induction for novel object categories"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "lsda large scale detection through adaptation"
    - "viewpoints and keypoints"
    - "simultaneous detection and segmentation"
    - "category specific object reconstruction from a single image"
    - "unsupervised discovery of mid level discriminative patches"

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)