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: "enabling pedestrian safety using computer vision techniques a case study of the 2018 uber inc self driving car crash"
  Abstract: Human lives are important. The decision to allow self-driving vehicles operate on our roads carries great weight. This has been a hot topic of debate between policy-makers, technologists and public safety institutions. The recent Uber Inc. self-driving car crash, resulting in the death of a pedestrian, has strengthened the argument that autonomous vehicle technology is still not ready for deployment on public roads. In this work, we analyze the Uber car crash and shed light on the question, "Could the Uber Car Crash have been avoided?". We apply state-of-the-art Computer Vision models to this highly practical scenario. More generally, our experimental results are an evaluation of various image enhancement and object recognition techniques for enabling pedestrian safety in low-lighting conditions using the Uber crash as a case study.

Connected through hyperedge HE-1 (induced by source paper "integrating deep reinforcement learning with model based path planners for automated driving"):
  with members:
    - "a deep reinforcement learning based driving policy for autonomous road vehicles"
    - "risky action recognition in lane change video clips using deep spatiotemporal networks with segmentation mask transfer"
    - "simulation based reinforcement learning for real world autonomous driving"
    - "a survey of autonomous driving common practices and emerging technologies"
    - "learning to drive in a day"
    - "carla an open urban driving simulator"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-2 (induced by source paper "predicting model failure using saliency maps in autonomous driving systems"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "visualbackprop efficient visualization of cnns"
    - "evaluating uncertainty quantification in end to end autonomous driving control"
    - "a human grounded evaluation benchmark for local explanations of machine learning"
    - "the elephant in the room"
    - "rigorous agent evaluation an adversarial approach to uncover catastrophic failures"
    - "not using the car to see the sidewalk quantifying and controlling the effects of context in classification and segmentation"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-3 (induced by source paper "fooling a real car with adversarial traffic signs"):
  with members:
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "exploring the space of black box attacks on deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "darts deceiving autonomous cars with toxic signs"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "synthesizing robust adversarial examples"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-4 (induced by source paper "adapting semantic segmentation models for changes in illumination and camera perspective"):
  with members:
    - "modeling camera effects to improve deep vision for real and synthetic data"
    - "autoaugment learning augmentation policies from data"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "data driven color augmentation techniques for deep skin image analysis"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

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)