human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "a lidar point cloud generator from a virtual world to autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "dels 3d deep localization and segmentation with a 3d semantic map":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "da4ad end to end deep attention aware features aided visual localization for autonomous driving")
  Member "complex yolo real time 3d object detection on point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "single stage object detection from top view grid maps on custom sensor setups")
  Member "semantic3d net a new large scale point cloud classification benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepsemantichppc hypothesis based planning over uncertain semantic point clouds")
  Member "carla an open urban driving simulator":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "an end to end system for crowdsourced 3d maps for autonomous vehicles the mapping component":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "simultaneous traffic sign detection and boundary estimation using convolutional neural network")
  Member "cnn for very fast ground segmentation in velodyne lidar data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description")
  Member "exploring spatial context for 3d semantic segmentation of point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving semantic analysis on point clouds via auxiliary supervision of local geometric priors")

Question: Please tell me which class the source paper 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)