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: "soilingnet soiling detection on automotive surround view cameras"
  Abstract: Cameras are an essential part of sensor suite in autonomous driving. Surround-view cameras are directly exposed to external environment and are vulnerable to get soiled. Cameras have a much higher degradation in performance due to soiling compared to other sensors. Thus it is critical to accurately detect soiling on the cameras, particularly for higher levels of autonomous driving. We created a new dataset having multiple types of soiling namely opaque and transparent. It will be released publicly as part of our WoodScape dataset \cite{yogamani2019woodscape} to encourage further research. We demonstrate high accuracy using a Convolutional Neural Network (CNN) based architecture. We also show that it can be combined with the existing object detection task in a multi-task learning framework. Finally, we make use of Generative Adversarial Networks (GANs) to generate more images for data augmentation and show that it works successfully similar to the style transfer.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "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"
    - "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 "dynamic task weighting methods for multi task networks in autonomous driving systems"):
  with members:
    - "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"
    - "real time joint object detection and semantic segmentation network for automated driving"
    - "which tasks should be learned together in multi task learning"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "taskonomy disentangling task transfer learning"
    - "autoaugment learning augmentation policies from data"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "end to end multi task learning with attention"

Connected through hyperedge HE-3 (induced by source paper "rainy screens collecting rainy datasets indoors"):
  with members:
    - "i can see clearly now image restoration via de raining"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "semantic foggy scene understanding with synthetic data"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (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"
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "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"
    - "semantic image synthesis with spatially adaptive normalization"

Connected through hyperedge HE-5 (induced by source paper "fusemodnet real time camera and lidar based moving object detection for robust low light autonomous driving"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "rgb and lidar fusion based 3d semantic segmentation for autonomous driving"
    - "fisheyemodnet moving object detection on surround view cameras for autonomous driving"
    - "rtseg real time semantic segmentation comparative study"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "hallucinating dense optical flow from sparse lidar for autonomous vehicles"
    - "illumination aware faster r cnn for robust multispectral pedestrian detection"
    - "shuffleseg real time semantic segmentation network"

Connected through hyperedge HE-6 (induced by source paper "fisheyemultinet real time multi task learning architecture for surround view automated parking system"):
  with members:
    - "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"
    - "woodscape a multi task multi camera fisheye dataset for autonomous driving"
    - "neurall towards a unified model for visual perception in automated driving"
    - "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"
    - "rtseg real time semantic segmentation comparative study"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-7 (induced by source paper "soildnet soiling degradation detection in autonomous driving"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "yuvmultinet real time yuv multi task cnn for autonomous driving"
    - "robust semantic segmentation in adverse weather conditions by means of sensor data fusion"
    - "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"

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)