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: "conditional adversarial synthesis of 3d facial action units"
  Abstract: Abstract   Employing deep learning-based approaches for fine-grained facial expression analysis, such as those involving the estimation of Action Unit (AU) intensities, is difficult due to the lack of a large-scale dataset of real faces with sufficiently diverse AU labels for training. In this paper, we consider how AU-level facial image synthesis can be used to substantially augment such a dataset. We propose an AU synthesis framework that combines the well-known 3D Morphable Model (3DMM), which intrinsically disentangles expression parameters from other face attributes, with models that adversarially generate 3DMM expression parameters conditioned on given target AU labels, in contrast to the more conventional approach of generating facial images directly. In this way, we are able to synthesize new combinations of expression parameters and facial images from desired AU labels. Extensive quantitative and qualitative results on the benchmark DISFA dataset and BP4D dataset demonstrate the effectiveness of our method on 3DMM facial expression parameter synthesis and data augmentation for deep learning-based AU intensity estimation.

Connected through hyperedge HE-1 (induced by source paper "region based adversarial synthesis of facial action units"):
  with members:
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "deep face recognition a survey"
    - "attention gan for object transfiguration in wild images"
    - "ganimation anatomically aware facial animation from a single image"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "exprgan facial expression editing with controllable expression intensity"
    - "age progression regression by conditional adversarial autoencoder"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"

Connected through hyperedge HE-2 (induced by source paper "geoconv geodesic guided convolution for facial action unit recognition"):
  with members:
    - "semantic relationships guided representation learning for facial action unit recognition"
    - "deep structure inference network for facial action unit recognition"
    - "depth aware cnn for rgb d segmentation"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "facial action unit detection using attention and relation learning"
    - "cnn based real time dense face reconstruction with inverse rendered photo realistic face images"
    - "deformable convolutional networks"
    - "sgdr stochastic gradient descent with restarts"

Connected through hyperedge HE-3 (induced by source paper "relation modeling with graph convolutional networks for facial action unit detection"):
  with members:
    - "semantic relationships guided representation learning for facial action unit recognition"
    - "bayesian semi supervised learning with graph gaussian processes"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "generative adversarial talking head bringing portraits to life with a weakly supervised neural network"):
  with members:
    - "end to end learning for 3d facial animation from raw waveforms of speech"
    - "triple generative adversarial nets"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "semantic facial expression editing using autoencoded flow"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "generative adversarial text to image synthesis"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"

Connected through hyperedge HE-5 (induced by source paper "deep adaptive attention for joint facial action unit detection and face alignment"):
  with members:
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "learning deep representation from coarse to fine for face alignment"

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)