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: "adversarially tuned scene generation"
  Abstract: Generalization performance of trained computer vision systems that use computer graphics (CG) generated data is not yet effective due to the concept of 'domain-shift' between virtual and real data. Although simulated data augmented with a few real world samples has been shown to mitigate domain shift and improve transferability of trained models, guiding or bootstrapping the virtual data generation with the distributions learnt from target real world domain is desired, especially in the fields where annotating even few real images is laborious (such as semantic labeling, and intrinsic images etc.). In order to address this problem in an unsupervised manner, our work combines recent advances in CG (which aims to generate stochastic scene layouts coupled with large collections of 3D object models) and generative adversarial training (which aims train generative models by measuring discrepancy between generated and real data in terms of their separability in the space of a deep discriminatively-trained classifier). Our method uses iterative estimation of the posterior density of prior distributions for a generative graphical model. This is done within a rejection sampling framework. Initially, we assume uniform distributions as priors on the parameters of a scene described by a generative graphical model. As iterations proceed the prior distributions get updated to distributions that are closer to the (unknown) distributions of target data. We demonstrate the utility of adversarially tuned scene generation on two real-world benchmark datasets (CityScapes and CamVid) for traffic scene semantic labeling with a deep convolutional net (DeepLab). We realized performance improvements by 2.28 and 3.14 points (using the IoU metric) between the DeepLab models trained on simulated sets prepared from the scene generation models before and after tuning to CityScapes and CamVid respectively.

Connected through hyperedge HE-1 (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"
    - "semantic aware grad gan for virtual to real urban scene adaption"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "what else can fool deep learning addressing color constancy errors on deep neural network performance"):
  with members:
    - "sensor independent illumination estimation for dnn models"
    - "spatially transformed adversarial examples"
    - "modeling camera effects to improve deep vision for real and synthetic data"
    - "autoaugment learning augmentation policies from data"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "unprocessing images for learned raw denoising"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "random erasing data augmentation"

Connected through hyperedge HE-3 (induced by source paper "learning to generate synthetic 3d training data through hybrid gradient"):
  with members:
    - "unrealrox an extremely photorealistic virtual reality environment for robotics simulations and synthetic data generation"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "gibson env real world perception for embodied agents"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "probabilistic model agnostic meta learning"

Connected through hyperedge HE-4 (induced by source paper "sada semantic adversarial diagnostic attacks for autonomous applications"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "towards analyzing semantic robustness of deep neural networks"
    - "deep drone racing learning agile flight in dynamic environments"
    - "semantic adversarial examples"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "yolov3 an incremental improvement"
    - "generating 3d adversarial point clouds"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"

Connected through hyperedge HE-5 (induced by source paper "srda generating instance segmentation annotation via scanning reasoning and domain adaptation"):
  with members:
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "synthesizing training data for object detection in indoor scenes"
    - "one sided unsupervised domain mapping"
    - "photographic image synthesis with cascaded refinement networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "mask r cnn"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-6 (induced by source paper "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "training augmentation with adversarial examples for robust speech recognition"
    - "unsupervised training for 3d morphable model regression"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "defense against the dark arts an overview of adversarial example security research and future research directions"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "motivating the rules of the game for adversarial example research"

Connected through hyperedge HE-7 (induced by source paper "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"):
  with members:
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "multimodal unsupervised image to image translation"
    - "modeling camera effects to improve deep vision for real and synthetic data"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "autoaugment learning augmentation policies from data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-8 (induced by source paper "modeling camera effects to improve visual learning from synthetic data"):
  with members:
    - "failing to learn autonomously identifying perception failures for self driving cars"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "expecting the unexpected training detectors for unusual pedestrians with adversarial imposters"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "unsupervised domain adaptation for semantic segmentation with gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "the cityscapes dataset for semantic urban scene understanding"

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)