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 "fusionmapping learning depth prediction with monocular images and 2d laser scans"):
  Abstract: Acquiring accurate three-dimensional depth information conventionally requires expensive multibeam LiDAR devices. Recently, researchers have developed a less expensive option by predicting depth information from two-dimensional color imagery. However, there still exists a substantial gap in accuracy between depth information estimated from two-dimensional images and real LiDAR point-cloud. In this paper, we introduce a fusion-based depth prediction method, called FusionMapping. This is the first method that fuses colored imagery and two-dimensional laser scan to estimate depth in-formation. More specifically, we propose an autoencoder-based depth prediction network and a novel point-cloud refinement network for depth estimation. We analyze the performance of our FusionMapping approach on the KITTI LiDAR odometry dataset and an indoor mobile robot system. The results show that our introduced approach estimates depth with better accuracy when compared to existing methods.

Includes members:
  Member "efficient tracking proposals using 2d 3d siamese networks on lidar":
    also connected through other hyperedges:
      (none)
  Member "pseudo lidar accurate depth for 3d object detection in autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "monocular 3d object detection with pseudo lidar point cloud":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "deep ordinal regression network for monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "pseudo lidar from visual depth estimation bridging the gap in 3d object detection for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "digging into self supervised monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation")
  Member "sparse to dense depth prediction from sparse depth samples and a single image":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "depth estimation by learning triangulation and densification of sparse points for multi view stereo")

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)