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 "rst modnet real time spatio temporal moving object detection for autonomous driving"):
  Abstract: Moving Object Detection (MOD) is a critical task for autonomous vehicles as moving objects represent higher collision risk than static ones. The trajectory of the ego-vehicle is planned based on the future states of detected moving objects. It is quite challenging as the ego-motion has to be modelled and compensated to be able to understand the motion of the surrounding objects. In this work, we propose a real-time end-to-end CNN architecture for MOD utilizing spatio-temporal context to improve robustness. We construct a novel time-aware architecture exploiting temporal motion information embedded within sequential images in addition to explicit motion maps using optical flow images.We demonstrate the impact of our algorithm on KITTI dataset where we obtain an improvement of 8% relative to the baselines. We compare our algorithm with state-of-the-art methods and achieve competitive results on KITTI-Motion dataset in terms of accuracy at three times better run-time. The proposed algorithm runs at 23 fps on a standard desktop GPU targeting deployment on embedded platforms.

Includes members:
  Member "semantickitti a dataset for semantic scene understanding of lidar sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review deep learning on 3d point clouds")
  Member "multinet multi stream feature aggregation and geometric loss strategy for multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic task weighting methods for multi task networks in autonomous driving systems")
  Member "neurall towards a unified model for visual perception in automated driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the road with 16 neurons mental imagery with bio inspired deep neural networks")
  Member "fusemodnet real time camera and lidar based moving object detection for robust low light autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi modality cascaded fusion technology for autonomous driving")
  Member "fisheyemodnet moving object detection on surround view cameras for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ds pass detail sensitive panoramic annular semantic segmentation through swaftnet for surrounding sensing")
  Member "real time dynamic object detection for autonomous driving using prior 3d maps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explorations and lessons learned in building an autonomous formula sae car from simulations")
  Member "rtseg real time semantic segmentation comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trained trajectory based automated parking system using visual slam")
  Member "shuffleseg real time semantic segmentation network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fusemodnet real time camera and lidar based moving object detection for robust low light autonomous driving")

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)