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: "self supervised generative adversarial networks"
  Abstract: Conditional GANs are at the forefront of natural image synthesis. The main drawback of such models is the necessity for labelled data. In this work we exploit two popular unsupervised learning techniques, adversarial training and self-supervision, to close the gap between conditional and unconditional GANs. In particular, we allow the networks to collaborate on the task of representation learning, while being adversarial with respect to the classic GAN game. The role of self-supervision is to encourage the discriminator to learn meaningful feature representations which are not forgotten during training. We test empirically both the quality of the learned image representations, and the quality of the synthesized images. Under the same conditions, the self-supervised GAN attains a similar performance to state-of-the-art conditional counterparts. Finally, we show that this approach to fully unsupervised learning can be scaled to attain an FID of 33 on unconditional ImageNet generation.

Connected through hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "s4l self supervised semi supervised learning"
    - "when does self supervision improve few shot learning"
    - "contrastive multiview coding"
    - "boosting few shot visual learning with self supervision"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "momentum contrast for unsupervised visual representation learning"

Connected through hyperedge HE-2 (induced by source paper "boosting few shot visual learning with self supervision"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "revisiting self supervised visual representation learning"
    - "meta learning for semi supervised few shot classification"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "unsupervised learning via meta learning"
    - "tadam task dependent adaptive metric for improved few shot learning"

Connected through hyperedge HE-3 (induced by source paper "d textbf s 3 l deep self semi supervised learning for image recognition"):
  with members:
    - "small data challenges in big data era a survey of recent progress on unsupervised and semi supervised methods"
    - "s4l self supervised semi supervised learning"
    - "a meta approach to defend noisy labels by the manifold regularizer psdr"
    - "unsupervised label noise modeling and loss correction"
    - "pushing the right boundaries matters wasserstein adversarial training for label noise"
    - "weight standardization"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-4 (induced by source paper "collaborative sampling in generative adversarial networks"):
  with members:
    - "collagan collaborative gan for missing image data imputation"
    - "theoretically principled trade off between robustness and accuracy"
    - "generative collaborative networks for single image super resolution"
    - "self supervised gans via auxiliary rotation loss"
    - "phase collaborative network for multi phase medical imaging segmentation"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "adversarial feedback loop"

Connected through hyperedge HE-5 (induced by source paper "mining gold samples for conditional gans"):
  with members:
    - "training discriminative models to evaluate generative ones"
    - "variational adversarial active learning"
    - "semantic image synthesis with spatially adaptive normalization"
    - "high fidelity image generation with fewer labels"
    - "cgans with projection discriminator"
    - "the relativistic discriminator a key element missing from standard gan"
    - "spectral normalization for generative adversarial networks"
    - "not all samples are created equal deep learning with importance sampling"

Connected through hyperedge HE-6 (induced by source paper "improving the harmony of the composite image by spatial separated attention module"):
  with members:
    - "inserting videos into videos"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "selective kernel networks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "temporally coherent video harmonization using adversarial networks"
    - "attention gan for object transfiguration in wild images"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-7 (induced by source paper "gazecorrection self guided eye manipulation in the wild using self supervised generative adversarial networks"):
  with members:
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "ganimation anatomically aware facial animation from a single image"
    - "everybody dance now"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (induced by source paper "the gan landscape losses architectures regularization and normalization"):
  with members:
    - "high fidelity image generation with fewer labels"
    - "pros and cons of gan evaluation measures"
    - "spectral normalization for generative adversarial networks"
    - "a large scale study on regularization and normalization in gans"
    - "deep generative models for distribution preserving lossy compression"
    - "a style based generator architecture for generative adversarial networks"
    - "on convergence and stability of gans"
    - "which training methods for gans do actually converge"

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)