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: "deep exemplar 2d 3d detection by adapting from real to rendered views"
  Abstract: This paper presents an end-to-end convolutional neural network (CNN) for 2D-3D exemplar detection. We demonstrate that the ability to adapt the features of natural images to better align with those of CAD rendered views is critical to the success of our technique. We show that the adaptation can be learned by compositing rendered views of textured object models on natural images. Our approach can be naturally incorporated into a CNN detection pipeline and extends the accuracy and speed benefits from recent advances in deep learning to 2D-3D exemplar detection. We applied our method to two tasks: instance detection, where we evaluated on the IKEA dataset, and object category detection, where we out-perform Aubry et al. for "chair" detection on a subset of the Pascal VOC dataset.

Connected through hyperedge HE-1 (induced by source paper "generating human action videos by coupling 3d game engines and probabilistic graphical models"):
  with members:
    - "pose guided human video generation"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "skeleton aided articulated motion generation"
    - "attentive semantic video generation using captions"
    - "carla an open urban driving simulator"
    - "temporal segment networks for action recognition in videos"

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"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "large scale historical watermark recognition dataset and a new consistency based approach"):
  with members:
    - "discovering visual patterns in art collections with spatially consistent feature learning"
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "neighbourhood consensus networks"
    - "identifying cross depicted historical motifs"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-4 (induced by source paper "meshadv adversarial meshes for visual recognition"):
  with members:
    - "spatially transformed adversarial examples"
    - "adversarial patch"
    - "yolov3 an incremental improvement"
    - "unsupervised training for 3d morphable model regression"
    - "generating adversarial examples with adversarial networks"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "learning from synthetic humans"

Connected through hyperedge HE-5 (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"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "improving face anti spoofing by 3d virtual synthesis"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "face synthesis for eyeglass robust face recognition"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "on the learning of deep local features for robust face spoofing detection"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "learning camera viewpoint using cnn to improve 3d body pose estimation"
    - "mocap guided data augmentation for 3d pose estimation in the wild"

Connected through hyperedge HE-7 (induced by source paper "joint embedding of 3d scan and cad objects"):
  with members:
    - "scan2cad learning cad model alignment in rgb d scans"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "matterport3d learning from rgb d data in indoor environments"
    - "stacked hourglass networks for human pose estimation"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "shapenet an information rich 3d model repository"
    - "learning deep object detectors from 3d models"

Connected through hyperedge HE-8 (induced by source paper "adadepth unsupervised content congruent adaptation for depth estimation"):
  with members:
    - "domain adaptation for visual applications a comprehensive survey"
    - "unsupervised learning of depth and ego motion from video"
    - "adversarial discriminative domain adaptation"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning from synthetic humans"
    - "semi supervised deep learning for monocular depth map prediction"
    - "unsupervised monocular depth estimation with left right consistency"

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)