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: "real to virtual domain unification for end to end autonomous driving"
  Abstract: In the spectrum of vision-based autonomous driving, vanilla end-to-end models are not interpretable and suboptimal in performance, while mediated perception models require additional intermediate representations such as segmentation masks or detection bounding boxes, whose annotation can be prohibitively expensive as we move to a larger scale. More critically, all prior works fail to deal with the notorious domain shift if we were to merge data collected from different sources, which greatly hinders the model generalization ability. In this work, we address the above limitations by taking advantage of virtual data collected from driving simulators, and present DU-drive, an unsupervised real-to-virtual domain unification framework for end-to-end autonomous driving. It first transforms real driving data to its less complex counterpart in the virtual domain and then predicts vehicle control commands from the generated virtual image. Our framework has three unique advantages: 1) it maps driving data collected from a variety of source distributions into a unified domain, effectively eliminating domain shift; 2) the learned virtual representation is simpler than the input real image and closer in form to the "minimum sufficient statistic" for the prediction task, which relieves the burden of the compression phase while optimizing the information bottleneck tradeoff and leads to superior prediction performance; 3) it takes advantage of annotated virtual data which is unlimited and free to obtain. Extensive experiments on two public driving datasets and two driving simulators demonstrate the performance superiority and interpretive capability of DU-drive.

Connected through hyperedge HE-1 (induced by source paper "deceiving image to image translation networks for autonomous driving with adversarial perturbations"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "data augmentation with symbolic to real image translation gans for traffic sign recognition"
    - "spatially transformed adversarial examples"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "multimodal unsupervised image to image translation"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-2 (induced by source paper "blending target domain adaptation by adversarial meta adaptation networks"):
  with members:
    - "unbiasing semantic segmentation for robot perception using synthetic data feature transfer"
    - "autoloss learning discrete schedules for alternate optimization"
    - "unsupervised multi target domain adaptation an information theoretic approach"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "multi target unsupervised domain adaptation without exactly shared categories"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "boosting domain adaptation by discovering latent domains"
    - "low shot learning from imaginary data"

Connected through hyperedge HE-3 (induced by source paper "visual based autonomous driving deployment from a stochastic and uncertainty aware perspective"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "multimodal unsupervised image to image translation"
    - "safe reinforcement learning with model uncertainty estimates"
    - "drit diverse image to image translation via disentangled representations"
    - "virtual to real reinforcement learning for autonomous driving"
    - "uncertainty aware learning from demonstration using mixture density networks with sampling free variance modeling"

Connected through hyperedge HE-4 (induced by source paper "toward characteristic preserving image based virtual try on network"):
  with members:
    - "viton an image based virtual try on network"
    - "dual motion gan for future flow embedded video prediction"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "pose guided person image generation"
    - "structured generative adversarial networks"
    - "convolutional neural network architecture for geometric matching"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"

Connected through hyperedge HE-5 (induced by source paper "cirl controllable imitative reinforcement learning for vision based self driving"):
  with members:
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "deep reinforcement learning that matters"
    - "virtual to real reinforcement learning for autonomous driving"
    - "parameter space noise for exploration"
    - "fast recurrent fully convolutional networks for direct perception in autonomous driving"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "carla an open urban driving simulator"
    - "attention aware face hallucination via deep reinforcement learning"

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)