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: "semantic aware grad gan for virtual to real urban scene adaption"
  Abstract: Recent advances in vision tasks (e.g., segmentation) highly depend on the availability of large-scale real-world image annotations obtained by cumbersome human labors. Moreover, the perception performance often drops significantly for new scenarios, due to the poor generalization capability of models trained on limited and biased annotations. In this work, we resort to transfer knowledge from automatically rendered scene annotations in virtual-world to facilitate real-world visual tasks. Although virtual-world annotations can be ideally diverse and unlimited, the discrepant data distributions between virtual and real-world make it challenging for knowledge transferring. We thus propose a novel Semantic-aware Grad-GAN (SG-GAN) to perform virtual-to-real domain adaption with the ability of retaining vital semantic information. Beyond the simple holistic color/texture transformation achieved by prior works, SG-GAN successfully personalizes the appearance adaption for each semantic region in order to preserve their key characteristic for better recognition. It presents two main contributions to traditional GANs: 1) a soft gradient-sensitive objective for keeping semantic boundaries; 2) a semantic-aware discriminator for validating the fidelity of personalized adaptions with respect to each semantic region. Qualitative and quantitative experiments demonstrate the superiority of our SG-GAN in scene adaption over state-of-the-art GANs. Further evaluations on semantic segmentation on Cityscapes show using adapted virtual images by SG-GAN dramatically improves segmentation performance than original virtual data. We release our code at this https URL

Connected through hyperedge HE-1 (induced by source paper "can giraffes become birds an evaluation of image to image translation for data generation"):
  with members:
    - "an efficient and layout independent automatic license plate recognition system based on the yolo detector"
    - "anda a novel data augmentation technique applied to salient object detection"
    - "masking salient object detection a mask region based convolutional neural network analysis for segmentation of salient objects"
    - "instagan instance aware image to image translation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-2 (induced by source paper "meta sim learning to generate synthetic datasets"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "virtualhome simulating household activities via programs"
    - "learning to simulate"
    - "multimodal unsupervised image to image translation"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "restyling data application to unsupervised domain adaptation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "a kernel theory of modern data augmentation"
    - "a domain agnostic normalization layer for unsupervised adversarial domain adaptation"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "artistic object recognition by unsupervised style adaptation"
    - "style augmentation data augmentation via style randomization"
    - "image to image translation for domain adaptation"
    - "no more discrimination cross city adaptation of road scene segmenters"

Connected through hyperedge HE-4 (induced by source paper "significance aware information bottleneck for domain adaptive semantic segmentation"):
  with members:
    - "attract or distract exploit the margin of open set"
    - "learning to adapt structured output space for semantic segmentation"
    - "a curriculum domain adaptation approach to the semantic segmentation of urban scenes"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "macro micro adversarial network for human parsing"

Connected through hyperedge HE-5 (induced by source paper "representation learning through latent canonicalizations"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "meta sim learning to generate synthetic datasets"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "a closed form solution to photorealistic image stylization"
    - "yolov3 an incremental improvement"
    - "unsupervised representation learning by predicting image rotations"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"

Connected through hyperedge HE-6 (induced by source paper "gan vs jpeg2000 image compression for distributed automotive perception higher peak snr does not mean better semantic segmentation"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "the perception distortion tradeoff"
    - "generating adversarial examples with adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning from synthetic data addressing domain shift for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "on scalable and efficient computation of large scale optimal transport"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "spectral normalization for generative adversarial networks"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "on computation and generalization of gans with spectrum control"

Connected through hyperedge HE-8 (induced by source paper "semantics aware image to image translation and domain transfer"):
  with members:
    - "travelgan image to image translation by transformation vector learning"
    - "harmonic unpaired image to image translation"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "sem gan semantically consistent image to image translation"
    - "image to image translation for cross domain disentanglement"
    - "image to image translation for domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "texturegan controlling deep image synthesis with texture patches"

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)