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: "neurall towards a unified model for visual perception in automated driving"
  Abstract: Convolutional Neural Networks (CNNs) are successfully used for the important automotive visual perception tasks including object recognition, motion and depth estimation, visual SLAM, etc. However, these tasks are typically independently explored and modeled. In this paper, we propose a joint multi-task network design for learning several tasks simultaneously. Our main motivation is the computational efficiency achieved by sharing the expensive initial convolutional layers between all tasks. Indeed, the main bottleneck in automated driving systems is the limited processing power available on deployment hardware. There is also some evidence for other benefits in improving accuracy for some tasks and easing development effort. It also offers scalability to add more tasks leveraging existing features and achieving better generalization. We survey various CNN based solutions for visual perception tasks in automated driving. Then we propose a unified CNN model for the important tasks and discuss several advanced optimization and architecture design techniques to improve the baseline model. The paper is partly review and partly positional with demonstration of several preliminary results promising for future research. We first demonstrate results of multi-stream learning and auxiliary learning which are important ingredients to scale to a large multi-task model. Finally, we implement a two-stream three-task network which performs better in many cases compared to their corresponding single-task models, while maintaining network size.

Connected through hyperedge HE-1 (induced by source paper "on the road with 16 neurons mental imagery with bio inspired deep neural networks"):
  with members:
    - "a survey of deep learning techniques for autonomous driving"
    - "yolov3 an incremental improvement"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "deep object centric policies for autonomous driving"
    - "recent advances in autoencoder based representation learning"
    - "advances in variational inference"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"

Connected through hyperedge HE-2 (induced by source paper "trained trajectory based automated parking system using visual slam"):
  with members:
    - "rtseg real time semantic segmentation comparative study"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-3 (induced by source paper "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"):
  with members:
    - "multi stream cnn based video semantic segmentation for automated driving"
    - "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "end to end multi task learning with attention"
    - "multi task learning as multi objective optimization"
    - "digrad multi task reinforcement learning with shared actions"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "flow guided feature aggregation for video object detection"

Connected through hyperedge HE-4 (induced by source paper "woodscape a multi task multi camera fisheye dataset for autonomous driving"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous 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 "rst modnet real time spatio temporal moving object detection for autonomous driving"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"
    - "fusemodnet real time camera and lidar based moving object detection for robust low light autonomous driving"
    - "fisheyemodnet moving object detection on surround view cameras for autonomous driving"
    - "real time dynamic object detection for autonomous driving using prior 3d maps"
    - "rtseg real time semantic segmentation comparative study"
    - "shuffleseg real time semantic segmentation network"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

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"
    - "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "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 "soilingnet soiling detection on automotive surround view cameras"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "real time joint object detection and semantic segmentation network for 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"

Connected through hyperedge HE-8 (induced by source paper "fisheyemodnet moving object detection on surround view cameras for autonomous driving"):
  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"
    - "rtseg real time semantic segmentation comparative study"
    - "shuffleseg real time semantic segmentation network"
    - "fusionseg learning to combine motion and appearance for fully automatic segmention of generic objects in videos"

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)