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: "generating photo realistic training data to improve face recognition accuracy"
  Abstract: In this paper we investigate the feasibility of using synthetic data to augment face datasets. In particular, we propose a novel generative adversarial network (GAN) that can disentangle identity-related attributes from non-identity-related attributes. This is done by training an embedding network that maps discrete identity labels to an identity latent space that follows a simple prior distribution, and training a GAN conditioned on samples from that distribution. Our proposed GAN allows us to augment face datasets by generating both synthetic images of subjects in the training set and synthetic images of new subjects not in the training set. By using recent advances in GAN training, we show that the synthetic images generated by our model are photo-realistic, and that training with augmented datasets can indeed increase the accuracy of face recognition models as compared with models trained with real images alone.

Connected through hyperedge HE-1 (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"
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-2 (induced by source paper "intra class variation isolation in conditional gans"):
  with members:
    - "cgans with projection discriminator"
    - "the gan landscape losses architectures regularization and normalization"
    - "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "deepvisage making face recognition simple yet with powerful generalization skills"

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)