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: "generate identity preserving faces by generative adversarial networks"
  Abstract: Generating identity-preserving faces aims to generate various face images keeping the same identity given a target face image. Although considerable generative models have been developed in recent years, it is still challenging to simultaneously acquire high quality of facial images and preserve the identity. Here we propose a compelling method using generative adversarial networks (GAN). Concretely, we leverage the generator of trained GAN to generate plausible faces and FaceNet as an identity-similarity discriminator to ensure the identity. Experimental results show that our method is qualified to generate both plausible and identity-preserving faces with high quality. In addition, our method provides a universal framework which can be realized in various ways by combining different face generators and identity-similarity discriminator.

Connected through hyperedge HE-1 (induced by source paper "flipped adversarial autoencoders"):
  with members:
    - "bidirectional conditional generative adversarial networks"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "invertible conditional gans for image editing"
    - "stacked generative adversarial networks"
    - "adversarial feature learning"
    - "membership inference attacks against machine learning models"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "face translation between images and videos using identity aware cyclegan"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "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"
    - "generative face completion"
    - "unsupervised image to image translation networks"
    - "multi view image generation from a single view"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "two birds with one stone transforming and generating facial images with iterative gan"):
  with members:
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "perceptual adversarial networks for image to image transformation"
    - "neural face editing with intrinsic image disentangling"
    - "age progression regression by conditional adversarial autoencoder"
    - "conditional generative adversarial nets"
    - "panda pose aligned networks for deep attribute modeling"
    - "face expression recognition and analysis the state of the art"

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)