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: "yes we gan applying adversarial techniques for autonomous driving"
  Abstract: Generative Adversarial Networks (GAN) have gained a lot of popularity from their introduction in 2014 till present. Research on GAN is rapidly growing and there are many variants of the original GAN focusing on various aspects of deep learning. GAN are perceived as the most impactful direction of machine learning in the last decade. This paper focuses on the application of GAN in autonomous driving including topics such as advanced data augmentation, loss function learning, semi-supervised learning, etc. We formalize and review key applications of adversarial techniques and discuss challenges and open problems to be addressed.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey"):
  with members:
    - "multi objective multi agent decision making a utility based analysis and survey"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "on first order meta learning algorithms"
    - "a tempt to unify heterogeneous driving databases using traffic primitives"

Connected through hyperedge HE-2 (induced by source paper "deceiving image to image translation networks for autonomous driving with adversarial perturbations"):
  with members:
    - "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"
    - "real to virtual domain unification for end to end autonomous driving"
    - "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-3 (induced by source paper "let s get dirty gan based data augmentation for soiling and adverse weather classification in autonomous driving"):
  with members:
    - "fisheyemultinet real time multi task learning architecture for surround view automated parking system"
    - "spatial attentive single image deraining with a high quality real rain dataset"
    - "progressive image deraining networks a better and simpler baseline"
    - "i can see clearly now image restoration via de raining"
    - "single image deraining a comprehensive benchmark analysis"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "semantic image synthesis with spatially adaptive normalization"

Connected through hyperedge HE-4 (induced by source paper "woodscape a multi task multi camera fisheye dataset for autonomous driving"):
  with members:
    - "neurall towards a unified model for visual perception in automated driving"
    - "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "less is more culling the training set to improve robustness of deep neural networks"
    - "deep learning is robust to massive label noise"
    - "rtseg real time semantic segmentation comparative study"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "an efficient optical flow based motion detection method for non stationary scenes"

Connected through hyperedge HE-5 (induced by source paper "soildnet soiling degradation detection in autonomous driving"):
  with members:
    - "yuvmultinet real time yuv multi task cnn for autonomous driving"
    - "robust semantic segmentation in adverse weather conditions by means of sensor data fusion"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "design of real time semantic segmentation decoder for automated driving"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "aggregated residual transformations for deep neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "soilingnet soiling detection on automotive surround view cameras"):
  with members:
    - "real time joint object detection and semantic segmentation network for automated driving"
    - "neurall towards a unified model for visual perception in automated driving"
    - "i can see clearly now image restoration via de raining"
    - "semantic foggy scene understanding with synthetic data"
    - "multimodal unsupervised image to image translation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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)