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: "a perceptual measure for deep single image camera calibration"
  Abstract: Most current single image camera calibration methods rely on specific image features or user input, and cannot be applied to natural images captured in uncontrolled settings. We propose inferring directly camera calibration parameters from a single image using a deep convolutional neural network. This network is trained using automatically generated samples from a large-scale panorama dataset, and considerably outperforms other methods, including recent deep learning-based approaches, in terms of standard L2 error. However, we argue that in many cases it is more important to consider how humans perceive errors in camera estimation. To this end, we conduct a large-scale human perception study where we ask users to judge the realism of 3D objects composited with and without ground truth camera calibration. Based on this study, we develop a new perceptual measure for camera calibration, and demonstrate that our deep calibration network outperforms other methods on this measure. Finally, we demonstrate the use of our calibration network for a number of applications including virtual object insertion, image retrieval and compositing.

Connected through hyperedge HE-1 (induced by source paper "uprightnet geometry aware camera orientation estimation from single images"):
  with members:
    - "learning the depths of moving people by watching frozen people"
    - "framenet learning local canonical frames of 3d surfaces from a single rgb image"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "groundnet segmentation aware monocular ground plane estimation with geometric consistency"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "deep depth completion of a single rgb d image"

Connected through hyperedge HE-2 (induced by source paper "car pose in context accurate pose estimation with ground plane constraints"):
  with members:
    - "a geometric approach to obtain a bird s eye view from an image"
    - "resolving 3d human pose ambiguities with 3d scene constraints"
    - "meta sim learning to generate synthetic datasets"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "a proposal based solution to spatio temporal action detection in untrimmed videos"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "groundnet monocular ground plane normal estimation with geometric consistency"

Connected through hyperedge HE-3 (induced by source paper "learning perspective undistortion of portraits"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "what does 2d geometric information really tell us about 3d face shape"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "synthesizing normalized faces from facial identity features"
    - "inversefacenet deep single shot inverse face rendering from a single image"
    - "towards large pose face frontalization in the wild"
    - "horizon lines in the wild"

Connected through hyperedge HE-4 (induced by source paper "temporally consistent horizon lines"):
  with members:
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "deep learning for vanishing point detection using an inverse gnomonic projection"
    - "improved regularization of convolutional neural networks with cutout"
    - "sgdr stochastic gradient descent with restarts"
    - "densely connected convolutional networks"
    - "horizon lines in the wild"
    - "temporally consistent motion segmentation from rgb d video"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-5 (induced by source paper "groundnet monocular ground plane normal estimation with geometric consistency"):
  with members:
    - "a baseline for 3d multi object tracking"
    - "real time correlation tracking via joint model compression and transfer"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "learning correspondence from the cycle consistency of time"
    - "unsupervised deep tracking"
    - "future near collision prediction from monocular video feasibility dataset and challenges"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "deep reinforcement learning for autonomous driving"

Connected through hyperedge HE-6 (induced by source paper "groundnet segmentation aware monocular ground plane estimation with geometric consistency"):
  with members:
    - "a baseline for 3d multi object tracking"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "learning correspondence from the cycle consistency of time"
    - "unsupervised deep tracking"
    - "future near collision prediction from monocular video feasibility dataset and challenges"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "deep reinforcement learning for autonomous driving"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"

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)