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: "production level facial performance capture using deep convolutional neural networks"
  Abstract: We present a real-time deep learning framework for video-based facial performance capture -- the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5-10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular video sequence of that subject. Since this 3D facial performance capture is fully automated, our system can drastically reduce the amount of labor involved in the development of modern narrative-driven video games or films involving realistic digital doubles of actors and potentially hours of animated dialogue per character. We compare our results with several state-of-the-art monocular real-time facial capture techniques and demonstrate compelling animation inference in challenging areas such as eyes and lips.

Connected through hyperedge HE-1 (induced by source paper "joint face detection and facial motion retargeting for multiple faces"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "mobileface 3d face reconstruction with efficient cnn regression"
    - "yolov3 an incremental improvement"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "self supervised cnn for unconstrained 3d facial performance capture from an rgb d camera"
    - "nonlinear 3d face morphable model"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"

Connected through hyperedge HE-2 (induced by source paper "3d hand shape and pose from images in the wild"):
  with members:
    - "learning pose specific representations by predicting different views"
    - "cross modal deep variational hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "back to rgb 3d tracking of hands and hand object interactions based on short baseline stereo"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "end to end recovery of human shape and pose"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "capture learning and synthesis of 3d speaking styles"):
  with members:
    - "expressive body capture 3d hands face and body from a single image"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "lip movements generation at a glance"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "visemenet audio driven animator centric speech animation"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "squad 100 000 questions for machine comprehension of text"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-4 (induced by source paper "learning to forecast and refine residual motion for image to video generation"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "cu net coupled u nets"
    - "cr gan learning complete representations for multi view generation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"

Connected through hyperedge HE-5 (induced by source paper "joint 3d face reconstruction and dense alignment with position map regression network"):
  with members:
    - "multi attribute robust component analysis for facial uv maps"
    - "side information for face completion a robust pca approach"
    - "densereg fully convolutional dense shape regression in the wild"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "end to end 3d face reconstruction with deep neural networks"
    - "learning dense facial correspondences in unconstrained images"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

Connected through hyperedge HE-6 (induced by source paper "learning category specific mesh reconstruction from image collections"):
  with members:
    - "dense 3d object reconstruction from a single depth view"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "end to end recovery of human shape and pose"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "hierarchical surface prediction for 3d object reconstruction"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"

Connected through hyperedge HE-7 (induced by source paper "deep energies for estimating three dimensional facial pose and expression"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "generative face completion"
    - "deep image prior"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-8 (induced by source paper "fml face model learning from videos"):
  with members:
    - "on learning 3d face morphable model from in the wild images"
    - "learning category specific mesh reconstruction from image collections"
    - "head reconstruction from internet photos"
    - "statistical transformer networks learning shape and appearance models via self supervision"
    - "voxceleb2 deep speaker recognition"
    - "nonlinear 3d face morphable model"
    - "building statistical shape spaces for 3d human modeling"
    - "unrestricted facial geometry reconstruction using image to image translation"

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)