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: "it takes only two adversarial generator encoder networks"
  Abstract: We present a new autoencoder-type architecture that is trainable in an unsupervised mode, sustains both generation and inference, and has the quality of conditional and unconditional samples boosted by adversarial learning. Unlike previous hybrids of autoencoders and adversarial networks, the adversarial game in our approach is set up directly between the encoder and the generator, and no external mappings are trained in the process of learning. The game objective compares the divergences of each of the real and the generated data distributions with the prior distribution in the latent space. We show that direct generator-vs-encoder game leads to a tight coupling of the two components, resulting in samples and reconstructions of a comparable quality to some recently-proposed more complex architectures.

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 "guided image generation with conditional invertible neural networks"):
  with members:
    - "videoflow a flow based generative model for video"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "analyzing inverse problems with invertible neural networks"
    - "training generative reversible networks"
    - "invertible residual networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "excessive invariance causes adversarial vulnerability"

Connected through hyperedge HE-3 (induced by source paper "large scale adversarial representation learning"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "data efficient image recognition with contrastive predictive coding"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"
    - "self supervised gans via auxiliary rotation loss"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-4 (induced by source paper "towards photographic image manipulation with balanced growing of generative autoencoders"):
  with members:
    - "the gan landscape losses architectures regularization and normalization"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "pioneer networks progressively growing generative autoencoder"
    - "cramer wold autoencoder"
    - "spectral normalization for generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "recursive chaining of reversible image to image translators for face aging"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-5 (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"
    - "learning inverse mapping by autoencoder based generative adversarial nets"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-6 (induced by source paper "multivariate information adversarial ensemble for scalable joint distribution matching"):
  with members:
    - "video to video synthesis"
    - "geometry score a method for comparing generative adversarial networks"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "soft gated warping gan for pose guided person image synthesis"
    - "modular generative adversarial networks"
    - "jointgan multi domain joint distribution learning with generative adversarial nets"
    - "style transfer from non parallel text by cross alignment"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-7 (induced by source paper "deep generative learning via variational gradient flow"):
  with members:
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "improved training of wasserstein gans"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "generalization and equilibrium in generative adversarial nets gans"

Connected through hyperedge HE-8 (induced by source paper "invert and defend model based approximate inversion of generative adversarial networks for secure inference"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "differentially private generative adversarial network"
    - "a style based generator architecture for generative adversarial networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

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)