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: "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data"
  Abstract: The fusion of color and lidar data plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs. While existing methods exploit redundant and complimentary information under good imaging conditions, they fail to do this in adverse weather and imaging conditions where the sensory streams can be asymmetrically distorted. These rare "edge-case" scenarios are not represented in available data sets, and existing fusion architectures are not designed to handle severe asymmetric distortions. We present a deep fusion architecture that allows for robust fusion in fog and snow without having large labeled training data available for these scenarios. Departing from proposal-level fusion, we propose a real-time single-shot model that adaptively fuses features driven by temporal coherence of the distortions. We validate the proposed method, trained on clean data, in simulation and on unseen conditions of in-the-wild driving scenarios.

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"
    - "gated2depth real time dense lidar from gated images"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"

Connected through hyperedge HE-2 (induced by source paper "mvloc multimodal variational geometry aware learning for visual localization"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "fuseseg lidar point cloud segmentation fusing multi modal data"
    - "sensor fusion using backward shortcut connections for sleep apnea detection in multi modal data"
    - "atloc attention guided camera localization"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "doubly reparameterized gradient estimators for monte carlo objectives"
    - "geometric loss functions for camera pose regression with deep learning"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "semantic understanding of foggy scenes with purely synthetic data"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"
    - "semantic nighttime image segmentation with synthetic stylized data gradual adaptation and uncertainty aware evaluation"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "semantic foggy scene understanding with synthetic data"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "deeptio a deep thermal inertial odometry with visual hallucination"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "keyframe based direct thermal inertial odometry"
    - "distilling knowledge from a deep pose regressor network"
    - "sganvo unsupervised deep visual odometry and depth estimation with stacked generative adversarial networks"
    - "deeppco end to end point cloud odometry through deep parallel neural network"
    - "deep auxiliary learning for visual localization and odometry"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"

Connected through hyperedge HE-5 (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"
    - "improved semantic stixels via multimodal sensor fusion"
    - "a benchmark for lidar sensors in fog is detection breaking down"
    - "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)