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: "transferring gans generating images from limited data"
  Abstract: Transferring the knowledge of pretrained networks to new domains by means of finetuning is a widely used practice for applications based on discriminative models. To the best of our knowledge this practice has not been studied within the context of generative deep networks. Therefore, we study domain adaptation applied to image generation with generative adversarial networks. We evaluate several aspects of domain adaptation, including the impact of target domain size, the relative distance between source and target domain, and the initialization of conditional GANs. Our results show that using knowledge from pretrained networks can shorten the convergence time and can significantly improve the quality of the generated images, especially when the target data is limited. We show that these conclusions can also be drawn for conditional GANs even when the pretrained model was trained without conditioning. Our results also suggest that density may be more important than diversity and a dataset with one or few densely sampled classes may be a better source model than more diverse datasets such as ImageNet or Places.

Connected through hyperedge HE-1 (induced by source paper "on leveraging pretrained gans for limited data generation"):
  with members:
    - "infinite brain mr images pggan based data augmentation for tumor detection"
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
    - "open event extraction from online text using a generative adversarial network"
    - "analyzing and improving the image quality of stylegan"
    - "generative adversarial network in medical imaging a review"
    - "a bert based transfer learning approach for hate speech detection in online social media"
    - "image generation from small datasets via batch statistics adaptation"

Connected through hyperedge HE-2 (induced by source paper "freeze discriminator a simple baseline for fine tuning gans"):
  with members:
    - "learning what and where to transfer"
    - "consistency regularization for generative adversarial networks"
    - "analyzing and improving the image quality of stylegan"
    - "relational knowledge distillation"
    - "momentum contrast for unsupervised visual representation learning"
    - "few shot adversarial learning of realistic neural talking head models"
    - "stargan v2 diverse image synthesis for multiple domains"
    - "few shot unsupervised image to image translation"

Connected through hyperedge HE-3 (induced by source paper "unbalanced gans pre training the generator of generative adversarial network using variational autoencoder"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "improved training of wasserstein gans"
    - "nips 2016 tutorial generative adversarial networks"
    - "energy based generative adversarial network"
    - "improved techniques for training gans"
    - "least squares generative adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "attentiongan unpaired image to image translation using attention guided generative adversarial networks"):
  with members:
    - "show attend and translate unsupervised image translation with self regularization and attention"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "attention guided generative adversarial networks for unsupervised image to image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-5 (induced by source paper "a novel bilevel paradigm for image to image translation"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "few shot segmentation propagation with guided networks"
    - "every smile is unique landmark guided diverse smile generation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-6 (induced by source paper "semantics disentangling for text to image generation"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "distractor aware siamese networks for visual object tracking"
    - "efficient video object segmentation via network modulation"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "large scale gan training for high fidelity natural image synthesis"
    - "image generation from scene graphs"
    - "fictitious gan training gans with historical models"

Connected through hyperedge HE-7 (induced by source paper "image generation from small datasets via batch statistics adaptation"):
  with members:
    - "cgans with projection discriminator"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "spectral normalization for generative adversarial networks"
    - "meta transfer learning for few shot learning"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"

Connected through hyperedge HE-8 (induced by source paper "model imitation for model based reinforcement learning"):
  with members:
    - "combating the compounding error problem with a multi step model"
    - "benchmarking model based reinforcement learning"
    - "when to trust your model model based policy optimization"
    - "minegan effective knowledge transfer from gans to target domains with few images"
    - "model ensemble trust region policy optimization"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "spectral normalization for generative adversarial networks"
    - "addressing function approximation error in actor critic methods"

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)