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: "kinshipgan synthesizing of kinship faces from family photos by regularizing a deep face network"
  Abstract: In this paper, we propose a kinship generator network that can synthesize a possible child face by analyzing his/her parent's photo. For this purpose, we focus on to handle the scarcity of kinship datasets throughout the paper by proposing novel solutions in particular. To extract robust features, we integrate a pre-trained face model to the kinship face generator. Moreover, the generator network is regularized with an additional face dataset and adversarial loss to decrease the overfitting of the limited samples. Lastly, we adapt cycle-domain transformation to attain a more stable results. Experiments are conducted on Families in the Wild (FIW) dataset. The experimental results show that the contributions presented in the paper provide important performance improvements compared to the baseline architecture and our proposed method yields promising perceptual results.

Connected through hyperedge HE-1 (induced by source paper "controllable descendant face synthesis"):
  with members:
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "unsupervised holistic image generation from key local patches"
    - "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"
    - "improved training of wasserstein gans"
    - "age progression regression by conditional adversarial autoencoder"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "makeup like a superstar deep localized makeup transfer network"

Connected through hyperedge HE-2 (induced by source paper "recognizing families in the wild rfiw the 4th edition"):
  with members:
    - "what will your child look like dna net age and gender aware kin face synthesizer"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "families in the wild fiw large scale kinship image database and benchmarks"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-3 (induced by source paper "what will your child look like dna net age and gender aware kin face synthesizer"):
  with members:
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "age progression regression by conditional adversarial autoencoder"
    - "invertible conditional gans for image editing"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "autoencoding beyond pixels using a learned similarity metric"
    - "adam a method for stochastic optimization"
    - "conditional generative adversarial nets"

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)