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 "self supervised learning for autonomous vehicles perception a conciliation between analytical and learning methods"):
  Abstract: This article mainly aims at motivating more investigations on self-supervised learning (SSL) perception techniques and their applications in autonomous driving. Such approaches are of broad interest as they can improve analytical methods performances, for example to perceive farther and more accurately spatially or temporally. In the meantime, they can also reduce the need of hand-labeled training data for learning methods, while offering the possibility to update the learning models into an online process. This can help an autonomous system to deal with unexpected changing conditions in the ego-vehicle environment. In all, this article firstly highlights the analytical and learning tools which may be interesting for improving or developping SSL techniques. Then, it presents the insights and correlations between existing autonomous driving perception SSL techniques, and some of their remaining limitations opening up some future research perspectives.

Includes members:
  Member "panoptic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "self supervised visual feature learning with deep neural networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "deep clustering for unsupervised learning of visual features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning representations by predicting bags of visual words")
  Member "dimensionality driven learning with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "digging into self supervised monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation")
  Member "self supervised learning for stereo matching with self improving ability":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "progressive fusion for unsupervised binocular depth estimation using cycled networks")
  Member "unsupervised learning of depth and ego motion from video":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rtm3d real time monocular 3d detection from object keypoints for 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)