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: "real time 3d face fitting and texture fusion on in the wild videos"
  Abstract: We present a fully automatic approach to real-time 3D face reconstruction from monocular in-the-wild videos. With the use of a cascaded-regressor-based face tracking and a 3D morphable face model shape fitting, we obtain a semidense 3D face shape. We further use the texture information from multiple frames to build a holistic 3D face representation from the video footage. Our system is able to capture facial expressions and does not require any person-specific training. We demonstrate the robustness of our approach on the challenging 300 Videos in the Wild (300-VW) dataset. Our real-time fitting framework is available as an open-source library at  http://4dface.org .

Connected through hyperedge HE-1 (induced by source paper "wing loss for robust facial landmark localisation with convolutional neural networks"):
  with members:
    - "evaluation of dense 3d reconstruction from 2d face images in the wild"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "sphereface deep hypersphere embedding for face recognition"
    - "end to end 3d face reconstruction with deep neural networks"
    - "pose invariant face alignment with a single cnn"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "dense face alignment"

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"
    - "face alignment in the wild a survey"
    - "facial performance capture with deep neural networks"
    - "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 "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"
    - "inversefacenet deep single shot inverse face rendering from a single image"
    - "face alignment in the wild a survey"
    - "facial performance capture with deep neural networks"

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)