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 gans via auxiliary rotation loss"
  Abstract: Conditional GANs are at the forefront of natural image synthesis. The main drawback of such models is the necessity for labeled data. In this work we exploit two popular unsupervised learning techniques, adversarial training and self-supervision, and take a step towards bridging 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 23.4 on unconditional ImageNet generation.

Connected through hyperedge HE-1 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "on mutual information maximization for representation learning"
    - "large scale adversarial representation learning"
    - "classification accuracy score for conditional generative models"
    - "data efficient image recognition with contrastive predictive coding"
    - "do imagenet classifiers generalize to imagenet"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"
    - "meta dataset a dataset of datasets for learning to learn from few examples"

Connected through hyperedge HE-2 (induced by source paper "inducing optimal attribute representations for conditional gans"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "multi label image recognition with graph convolutional networks"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "auglabel exploiting word representations to augment labels for face attribute classification"
    - "biphasic learning of gans for high resolution image to image translation"
    - "cgans with projection discriminator"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "graph neural networks a review of methods and applications"

Connected through hyperedge HE-3 (induced by source paper "a u net based discriminator for generative adversarial networks"):
  with members:
    - "consistency regularization for generative adversarial networks"
    - "analyzing and improving the image quality of stylegan"
    - "coco gan generation by parts via conditional coordinating"
    - "mixmatch a holistic approach to semi supervised learning"
    - "cgans with projection discriminator"
    - "the gan landscape losses architectures regularization and normalization"
    - "pa gan improving gan training by progressive augmentation"
    - "on line adaptative curriculum learning for gans"

Connected through hyperedge HE-4 (induced by source paper "self supervised spatio temporal representation learning using variable playback speed prediction"):
  with members:
    - "video cloze procedure for self supervised spatio temporal learning"
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "unsupervised domain adaptation through self supervision"
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "group normalization"
    - "unsupervised representation learning by predicting image rotations"
    - "slowfast networks for video recognition"
    - "self supervised video representation learning with space time cubic puzzles"

Connected through hyperedge HE-5 (induced by source paper "a multi view perspective of self supervised learning"):
  with members:
    - "rethinking data augmentation self supervision and self distillation"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "unsupervised domain adaptation through self supervision"
    - "revisiting self supervised visual representation learning"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-6 (induced by source paper "noise robust generative adversarial networks"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "noise2self blind denoising by self supervision"
    - "diversity sensitive conditional generative adversarial networks"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"

Connected through hyperedge HE-7 (induced by source paper "self supervised gan analysis and improvement with multi class minimax game"):
  with members:
    - "an improved self supervised gan via adversarial training"
    - "improving gan with neighbors embedding and gradient matching"
    - "varying k lipschitz constraint for generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "rob gan generator discriminator and adversarial attacker"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised representation learning by predicting image rotations"
    - "dist gan an improved gan using distance constraints"

Connected through hyperedge HE-8 (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"
    - "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"
    - "defense gan protecting classifiers against adversarial attacks using generative models"

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)