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 "perception and decision making of autonomous systems in the era of learning an overview"):
  Abstract: The ability of inferring its own ego-motion, autonomous understanding the surroundings and planning trajectory are the features of autonomous robot systems. Therefore, in order to improve the perception and decision-making ability of autonomous robot systems, Simultaneous localization and mapping (SLAM) and autonomous trajectory planning are widely researched, especially combining learning-based algorithms. #R##N#In this paper, we mainly focus on the application of learning-based approaches to perception and decision-making, and survey the current state-of-the-art SLAM and trajectory planning algorithms. Firstly, we delineate the existing classical SLAM solutions and review deep learning-based methods of environment perception and understanding, including deep learning-based monocular depth estimation, ego-motion prediction, image enhancement, object detection, semantic segmentation and their combinations with previous SLAM frameworks. Secondly, we summarize the existing path planning and trajectory planning methods and survey the navigation methods based on reinforcement learning. Finally, several challenges and promising directions are concluded to related researchers for future work in both autonomous perception and decision-making.

Includes members:
  Member "selective sensor fusion for neural visual inertial odometry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mvloc multimodal variational geometry aware learning for visual localization")
  Member "learning monocular depth estimation infusing traditional stereo knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self supervised monocular depth hints")
  Member "deep direct visual odometry":
    also connected through other hyperedges:
      (none)
  Member "fastdepth fast monocular depth estimation on embedded systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structured knowledge distillation for dense prediction")
  Member "recurrent neural network for un supervised learning of monocular videovisual odometry and depth":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "multi frame gan image enhancement for stereo visual odometry in low light":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry")
  Member "unsupervised scale consistent depth and ego motion learning from monocular video":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry")
  Member "sganvo unsupervised deep visual odometry and depth estimation with stacked generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deeptio a deep thermal inertial odometry with visual hallucination")

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)