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: "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
  Abstract: We propose a novel end-to-end semi-supervised adversarial framework to generate photorealistic face images of new identities with a wide range of expressions, poses, and illuminations conditioned by synthetic images sampled from a 3D morphable model. Previous adversarial style-transfer methods either supervise their networks with a large volume of paired data or train highly under-constrained two-way generative networks in an unsupervised fashion. We propose a semi-supervised adversarial learning framework to constrain the two-way networks by a small number of paired real and synthetic images, along with a large volume of unpaired data. A set-based loss is also proposed to preserve identity coherence of generated images. Qualitative results show that generated face images of new identities contain pose, lighting and expression diversity. They are also highly constrained by the synthetic input images while adding photorealism and retaining identity information. We combine face images generated by the proposed method with a real data set to train face recognition algorithms and evaluate the model quantitatively on two challenging data sets: LFW and IJB-A. The generated images by our framework consistently improve the performance of deep face recognition networks trained with the Oxford VGG Face dataset, and achieve comparable results to the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "inducing optimal attribute representations for conditional gans"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "multi label image recognition with graph convolutional networks"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "auglabel exploiting word representations to augment labels for face attribute classification"
    - "biphasic learning of gans for high resolution image to image translation"
    - "cgans with projection discriminator"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "graph neural networks a review of methods and applications"

Connected through hyperedge HE-2 (induced by source paper "synthesizing coupled 3d face modalities by trunk branch generative adversarial networks"):
  with members:
    - "meshgan non linear 3d morphable models of faces"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "representation learning by rotating your faces"
    - "3dfacegan adversarial nets for 3d face representation generation and translation"
    - "combining 3d morphable models a large scale face and head model"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "arcface additive angular margin loss for deep face recognition"
    - "towards open set identity preserving face synthesis"

Connected through hyperedge HE-3 (induced by source paper "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "unsupervised training for 3d morphable model regression"
    - "3d face reconstruction with geometry details from a single image"
    - "deep appearance models for face rendering"
    - "high quality facial surface and texture synthesis via generative adversarial networks"
    - "nonlinear 3d face morphable model"
    - "progressive growing of gans for improved quality stability and variation"
    - "unrestricted facial geometry reconstruction using image to image translation"

Connected through hyperedge HE-4 (induced by source paper "sampling strategies for gan synthetic data"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "how good is my gan"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "deep facial expression recognition a survey"
    - "autoaugment learning augmentation policies from data"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "improving facial attribute prediction using semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "synthesizing facial photometries and corresponding geometries using generative adversarial networks"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "densereg fully convolutional dense shape regression in the wild"
    - "efficient inter geodesic distance computation and fast classical scaling"
    - "high quality facial surface and texture synthesis via generative adversarial networks"
    - "nonlinear 3d face morphable model"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "deformable shape completion with graph convolutional autoencoders"

Connected through hyperedge HE-6 (induced by source paper "auglabel exploiting word representations to augment labels for face attribute classification"):
  with members:
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "improving facial attribute prediction using semantic segmentation"
    - "regularizing neural networks by penalizing confident output distributions"
    - "disturblabel regularizing cnn on the loss layer"
    - "cp mtml coupled projection multi task metric learning for large scale face retrieval"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "ask your neurons a deep learning approach to visual question answering"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-7 (induced by source paper "face de occlusion using 3d morphable model and generative adversarial network"):
  with members:
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "geometry aware face completion and editing"
    - "3d face reconstruction with geometry details from a single image"
    - "generative image inpainting with contextual attention"
    - "generative face completion"
    - "extreme 3d face reconstruction seeing through occlusions"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "uv gan adversarial facial uv map completion for pose invariant face recognition"

Connected through hyperedge HE-8 (induced by source paper "high quality facial surface and texture synthesis via generative adversarial networks"):
  with members:
    - "progressive growing of gans for improved quality stability and variation"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "do we really need to collect millions of faces for effective face recognition"
    - "learning from simulated and unsupervised images through adversarial training"
    - "least squares generative adversarial networks"
    - "image to image translation with conditional adversarial 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)