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: "a benchmark for lidar sensors in fog is detection breaking down"
  Abstract: Autonomous driving at level five does not only means self-driving in the sunshine. Adverse weather is especially critical because fog, rain, and snow degrade the perception of the environment. In this work, current state of the art light detection and ranging (lidar) sensors are tested in controlled conditions in a fog chamber. We present current problems and disturbance patterns for four different state of the art lidar systems. Moreover, we investigate how tuning internal parameters can improve their performance in bad weather situations. This is of great importance because most state of the art detection algorithms are based on undisturbed lidar data.

Connected through hyperedge HE-1 (induced by source paper "uncertainty depth estimation with gated images for 3d reconstruction"):
  with members:
    - "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather"
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "learning single camera depth estimation using dual pixels"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "uncertainty estimation in one stage object detection"
    - "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data"
    - "gated2depth real time dense lidar from gated images"

Connected through hyperedge HE-2 (induced by source paper "pixel accurate depth evaluation in realistic driving scenarios"):
  with members:
    - "depth coefficients for depth completion"
    - "pixor real time 3d object detection from point clouds"
    - "learning the depths of moving people by watching frozen people"
    - "gated2depth real time dense lidar from gated images"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "semantic foggy scene understanding with synthetic data"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "rethinking monocular depth estimation with adversarial training"

Connected through hyperedge HE-3 (induced by source paper "cnn based lidar point cloud de noising in adverse weather"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "weather influence and classification with automotive lidar sensors"
    - "pixel accurate depth evaluation in realistic driving scenarios"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data"
    - "improved semantic stixels via multimodal sensor fusion"
    - "pointpillars fast encoders for object detection from point clouds"
    - "noise2noise learning image restoration without clean data"

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)