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: "precise synthetic image and lidar presil dataset for autonomous vehicle perception"
  Abstract: We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large detailed world with realistic graphics, which provides a diverse data collection environment. Existing works creating synthetic LiDAR data for autonomous driving with GTA V have not released their datasets, rely on an in-game raycasting function which represents people as cylinders, and can fail to capture vehicles past 30 metres. Our work creates a precise LiDAR simulator within GTA V which collides with detailed models for all entities no matter the type or position. The PreSIL dataset consists of over 50,000 frames and includes high-definition images with full resolution depth information, semantic segmentation (images), point-wise segmentation (point clouds), and detailed annotations for all vehicles and people. Collecting additional data with our framework is entirely automatic and requires no human annotation of any kind. We demonstrate the effectiveness of our dataset by showing an improvement of up to 5% average precision on the KITTI 3D Object Detection benchmark challenge when state-of-the-art 3D object detection networks are pre-trained with our data. The data and code are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "adapting object detectors with conditional domain normalization"):
  with members:
    - "compound domain adaptation in an open world"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "domain adaptation for structured output via discriminative patch representations"
    - "semantic image synthesis with spatially adaptive normalization"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "pointdan a multi scale 3d domain adaption network for point cloud representation"
    - "moment matching for multi source domain adaptation"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "fantrack 3d multi object tracking with feature association network"):
  with members:
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "end to end learning of multi sensor 3d tracking by detection"
    - "mono camera 3d multi object tracking using deep learning detections and pmbm filtering"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "focal loss for dense object detection"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"
    - "online multi target tracking using recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "trupercept trust modelling for autonomous vehicle cooperative perception from synthetic data"):
  with members:
    - "cooper cooperative perception for connected autonomous vehicles based on 3d point clouds"
    - "bayesod a bayesian approach for uncertainty estimation in deep object detectors"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "carla an open urban driving simulator"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"

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)