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: "video inpainting by jointly learning temporal structure and spatial details"
  Abstract: We present a new data-driven video inpainting method for recovering missing regions of video frames. A novel deep learning architecture is proposed which contains two sub-networks: a temporal structure inference network and a spatial detail recovering network. The temporal structure inference network is built upon a 3D fully convolutional architecture: it only learns to complete a low-resolution video volume given the expensive computational cost of 3D convolution. The low resolution result provides temporal guidance to the spatial detail recovering network, which performs image-based inpainting with a 2D fully convolutional network to produce recovered video frames in their original resolution. Such two-step network design ensures both the spatial quality of each frame and the temporal coherence across frames. Our method jointly trains both sub-networks in an end-to-end manner. We provide qualitative and quantitative evaluation on three datasets, demonstrating that our method outperforms previous learning-based video inpainting methods.

Connected through hyperedge HE-1 (induced by source paper "do as i do transferring human motion and appearance between monocular videos with spatial and temporal constraints"):
  with members:
    - "learning character agnostic motion for motion retargeting in 2d"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "video to video synthesis"
    - "synthesizing images of humans in unseen poses"
    - "deep video based performance cloning"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "video based reconstruction of 3d people models"

Connected through hyperedge HE-2 (induced by source paper "kernel quantization for efficient network compression"):
  with members:
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "towards the limit of network quantization"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-3 (induced by source paper "deep frequent spatial temporal learning for face anti spoofing"):
  with members:
    - "specular and diffuse reflection based face liveness detection for mobile devices"
    - "learning generalizable and identity discriminative representations for face anti spoofing"
    - "feathernets convolutional neural networks as light as feather for face anti spoofing"
    - "face de spoofing anti spoofing via noise modeling"
    - "discriminative representation combinations for accurate face spoofing detection"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "a dataset and benchmark for large scale multi modal face anti spoofing"
    - "joint 3d face reconstruction and dense alignment with position map regression network"

Connected through hyperedge HE-4 (induced by source paper "preserving semantic and temporal consistency for unpaired video to video translation"):
  with members:
    - "inserting videos into videos"
    - "deep video inpainting"
    - "deep blind video decaptioning by temporal aggregation and recurrence"
    - "frame recurrent video super resolution"
    - "video to video synthesis"
    - "recycle gan unsupervised video retargeting"
    - "multimodal unsupervised image to image translation"
    - "learning blind video temporal consistency"

Connected through hyperedge HE-5 (induced by source paper "attentive action and context factorization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "non local neural networks"
    - "attention is all you need"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "unsupervised learning of disentangled representations from video"
    - "excitation backprop for rnns"

Connected through hyperedge HE-6 (induced by source paper "deep long audio inpainting"):
  with members:
    - "deep video inpainting"
    - "adversarial generation of time frequency features with application in audio synthesis"
    - "free form video inpainting with 3d gated convolution and temporal patchgan"
    - "learnable gated temporal shift module for deep video inpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "image inpainting for irregular holes using partial convolutions"
    - "a context encoder for audio inpainting"
    - "efficient neural audio synthesis"

Connected through hyperedge HE-7 (induced by source paper "free form video inpainting with 3d gated convolution and temporal patchgan"):
  with members:
    - "deep video inpainting"
    - "learnable gated temporal shift module for deep video inpainting"
    - "vornet spatio temporally consistent video inpainting for object removal"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "video to video synthesis"
    - "image inpainting for irregular holes using partial convolutions"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "two phase hair image synthesis by self enhancing generative model"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "faceshop deep sketch based face image editing"
    - "learning dual convolutional neural networks for low level vision"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "residual dense network for image super resolution"
    - "mganet a robust model for quality enhancement of compressed video"
    - "progressive growing of gans for improved quality stability and variation"

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)