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: "improved semantic stixels via multimodal sensor fusion"
  Abstract: This paper presents a compact and accurate representation of 3D scenes that are observed by a LiDAR sensor and a monocular camera. The proposed method is based on the well-established Stixel model originally developed for stereo vision applications. We extend this Stixel concept to incorporate data from multiple sensor modalities. The resulting mid-level fusion scheme takes full advantage of the geometric accuracy of LiDAR measurements as well as the high resolution and semantic detail of RGB images. The obtained environment model provides a geometrically and semantically consistent representation of the 3D scene at a significantly reduced amount of data while minimizing information loss at the same time. Since the different sensor modalities are considered as input to a joint optimization problem, the solution is obtained with only minor computational overhead. We demonstrate the effectiveness of the proposed multimodal Stixel algorithm on a manually annotated ground truth dataset. Our results indicate that the proposed mid-level fusion of LiDAR and camera data improves both the geometric and semantic accuracy of the Stixel model significantly while reducing the computational overhead as well as the amount of generated data in comparison to using a single modality on its own.

Connected through hyperedge HE-1 (induced by source paper "multimodal end to end autonomous driving"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "towards self supervised high level sensor fusion"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "deep ordinal regression network for monocular depth estimation"
    - "pointpillars fast encoders for object detection from point clouds"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"

Connected through hyperedge HE-2 (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"
    - "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)