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: "facial performance capture with deep neural networks"
  Abstract: We present a deep learning technique for facial performance capture, i.e., the transfer of video footage into a motion sequence of a 3D mesh representing an actor's face. Specifically, we build on a conventional capture pipeline based on computer vision and multi-view video, and use its results to train a deep neural network to produce similar output from a monocular video sequence. Once trained, our network produces high-quality results for unseen inputs with greatly reduced effort compared to the conventional system. #R##N#In practice, we have found that approximately 10 minutes worth of high-quality data is sufficient for training a network that can then automatically process as much footage from video to 3D as needed. This yields major savings in the development of modern narrative-driven video games involving digital doubles of actors and potentially hours of animated dialogue per character.

Connected through hyperedge HE-1 (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-2 (induced by source paper "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "real time 3d face fitting and texture fusion on in the wild videos"
    - "face alignment in the wild a survey"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "production level facial performance capture using deep convolutional neural networks"
    - "deep cascaded bi network for face hallucination"
    - "robust lstm autoencoders for face de occlusion in the wild"
    - "perspective transformer nets learning single view 3d object reconstruction without 3d supervision"

Connected through hyperedge HE-3 (induced by source paper "inversefacenet deep single shot inverse face rendering from a single image"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "3d face reconstruction with geometry details from a single image"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "3d face morphable models in the wild"
    - "face alignment in the wild a survey"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "failure detection for facial landmark detectors"
    - "production level facial performance capture using deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "self supervised multi level face model learning for monocular reconstruction at over 250 hz"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "3d morphable models as spatial transformer networks"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "neural face editing with intrinsic image disentangling"
    - "real time 3d face fitting and texture fusion on in the wild videos"
    - "inversefacenet deep single shot inverse face rendering from a single image"
    - "face alignment in the wild a survey"

Connected through hyperedge HE-5 (induced by source paper "learning dense facial correspondences in unconstrained images"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "production level facial performance capture using deep convolutional neural networks"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "photorealistic facial texture inference using deep neural networks"
    - "learning dense correspondence via 3d guided cycle consistency"
    - "finding tiny faces"

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)