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: "learning inverse mapping by autoencoder based generative adversarial nets"
  Abstract: The inverse mapping of GANs'(Generative Adversarial Nets) generator has a great potential value.Hence, some works have been developed to construct the inverse function of generator by directly learning or adversarial learning.While the results are encouraging, the problem is highly challenging and the existing ways of training inverse models of GANs have many disadvantages, such as hard to train or poor performance.Due to these reasons, we propose a new approach based on using inverse generator ($IG$) model as encoder and pre-trained generator ($G$) as decoder of an AutoEncoder network to train the $IG$ model. In the proposed model, the difference between the input and output, which are both the generated image of pre-trained GAN's generator, of AutoEncoder is directly minimized. The optimizing method can overcome the difficulty in training and inverse model of an non one-to-one function.We also applied the inverse model of GANs' generators to image searching and translation.The experimental results prove that the proposed approach works better than the traditional approaches in image searching.

Connected through hyperedge HE-1 (induced by source paper "lia latently invertible autoencoder with adversarial learning"):
  with members:
    - "generative latent flow a framework for non adversarial image generation"
    - "implicit autoencoders"
    - "large scale adversarial representation learning"
    - "interpreting the latent space of gans for semantic face editing"
    - "adversarial training of partially invertible variational autoencoders"
    - "pioneer networks progressively growing generative autoencoder"
    - "f vaes improve vaes with conditional flows"
    - "noise2noise learning image restoration without clean data"

Connected through hyperedge HE-2 (induced by source paper "semi supervised image attribute editing using generative adversarial networks"):
  with members:
    - "pioneer networks progressively growing generative autoencoder"
    - "spectral normalization for generative adversarial networks"
    - "a style based generator architecture for generative adversarial networks"
    - "on convergence and stability of gans"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "it takes only two adversarial generator encoder networks"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-3 (induced by source paper "improving password guessing via representation learning"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "graph embedding techniques applications and performance a survey"
    - "large scale gan training for high fidelity natural image synthesis"
    - "passgan a deep learning approach for password guessing"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "sampling generative networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "exploiting gan internal capacity for high quality reconstruction of natural images"):
  with members:
    - "large scale gan training for high fidelity natural image synthesis"
    - "non local neural networks"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "neural photo editing with introspective adversarial networks"
    - "unrolled generative adversarial networks"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "adversarial feature learning"

Connected through hyperedge HE-5 (induced by source paper "source generator attribution via inversion"):
  with members:
    - "detecting overfitting of deep generative networks via latent recovery"
    - "do gans leave artificial fingerprints"
    - "learning to detect fake face images in the wild"
    - "attributing fake images to gans analyzing fingerprints in generated images"
    - "spectral normalization for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "generative visual manipulation on the natural image manifold"

Connected through hyperedge HE-6 (induced by source paper "adversarial training in affective computing and sentiment analysis recent advances and perspectives"):
  with members:
    - "geometry contrastive gan for facial expression transfer"
    - "generative adversarial networks an overview"
    - "towards open set identity preserving face synthesis"
    - "talking face generation by conditional recurrent adversarial network"
    - "generative adversarial talking head bringing portraits to life with a weakly supervised neural network"
    - "interactive generative adversarial networks for facial expression generation in dyadic interactions"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "voice impersonation using generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "pioneer networks progressively growing generative autoencoder"):
  with members:
    - "cramer wold autoencoder"
    - "spectral normalization for generative adversarial networks"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "it takes only two adversarial generator encoder networks"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "stabilizing training of generative adversarial networks through regularization"

Connected through hyperedge HE-8 (induced by source paper "inverting the generator of a generative adversarial network ii"):
  with members:
    - "improved techniques for training gans"
    - "adversarial feature learning"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "generative visual manipulation on the natural image manifold"
    - "task specific adversarial cost function"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "understanding deep image representations by inverting them"

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.LG(Machine Learning)