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: "real time dynamic object detection for autonomous driving using prior 3d maps"
  Abstract: Lidar has become an essential sensor for autonomous driving as it provides reliable depth estimation. Lidar is also the primary sensor used in building 3D maps which can be used even in the case of low-cost systems which do not use Lidar. Computation on Lidar point clouds is intensive as it requires processing of millions of points per second. Additionally there are many subsequent tasks such as clustering, detection, tracking and classification which makes real-time execution challenging. In this paper, we discuss real-time dynamic object detection algorithms which leverages previously mapped Lidar point clouds to reduce processing. The prior 3D maps provide a static background model and we formulate dynamic object detection as a background subtraction problem. Computation and modeling challenges in the mapping and online execution pipeline are described. We propose a rejection cascade architecture to subtract road regions and other 3D regions separately. We implemented an initial version of our proposed algorithm and evaluated the accuracy on CARLA simulator.

Connected through hyperedge HE-1 (induced by source paper "rst modnet real time spatio temporal moving object detection for autonomous driving"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"
    - "neurall towards a unified model for visual perception in automated driving"
    - "fusemodnet real time camera and lidar based moving object detection for robust low light autonomous driving"
    - "fisheyemodnet moving object detection on surround view cameras for autonomous driving"
    - "rtseg real time semantic segmentation comparative study"
    - "shuffleseg real time semantic segmentation network"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-2 (induced by source paper "explorations and lessons learned in building an autonomous formula sae car from simulations"):
  with members:
    - "closing the gap towards end to end autonomous vehicle system"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "learning end to end autonomous driving using guided auxiliary supervision"
    - "design of an autonomous racecar perception state estimation and system integration"
    - "virtual to real reinforcement learning for autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "multimodal 3d object detection from simulated pretraining"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "carla an open urban driving simulator"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "end to end driving via conditional imitation learning"
    - "multi view 3d object detection network for autonomous driving"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "augmented lidar simulator for autonomous driving"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "meta sim learning to generate synthetic datasets"
    - "iou loss for 2d 3d object detection"
    - "mapless online detection of dynamic objects in 3d lidar"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"
    - "structured domain randomization bridging the reality gap by context aware synthetic 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)