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 "conf net predicting depth completion error map for high confidence dense 3d point cloud"):
  Abstract: This work proposes a new method for depth completion of sparse LiDAR data using a convolutional neural network which learns to generate almost full 3D point-clouds with significantly lower root mean squared error (RMSE) over state-of-the-art methods. An almost dense high-confidence/low-variance point-cloud is more valuable for safety-critical applications specifically real-world autonomous driving than a dense point-cloud with high error rate and high variance. We examine the error of the standard depth completion methods and demonstrate that the error exhibits a long tail distribution which can be significantly reduced if a small portion of the generated depth points can be identified and removed. We add a purging step to our neural network and present a novel end-to-end algorithm that learns to predict a high-quality error-map of its prediction. Using our predicted error map, we demonstrate that by up-filling a LiDAR point cloud from 18,000 points to 285,000 points, versus 300,000 points for full depth, we can reduce the RMSE error from 1004 to 399. This error is approximately 60% less than the state-of-the-art and 50% less than the state-of-the-art with RGB guidance. We only need to remove 0.3% of the predicted points to get comparable results with the state-of-the-art which has RGB guidance. Our post-processing step takes the output of a standard encoder-decoder network, to generate high resolution 360 degrees dense point-cloud. In addition to analyzing our results on Kitti depth completion dataset, we demonstrate the real-world performance of our algorithm using data gathered with a Velodyne VLP-32C LiDAR mounted on our vehicle to verify the effectiveness and real-time performance of our algorithm for autonomous driving. Codes and demo videos are available at this http URL.

Includes members:
  Member "sparse and noisy lidar completion with rgb guidance and uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "confidence propagation through cnns for guided sparse depth regression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "generating and exploiting probabilistic monocular depth estimates":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "conf net toward high confidence dense 3d point cloud with error map prediction")
  Member "detail preserving depth estimation from a single image using attention guided networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "conf net toward high confidence dense 3d point cloud with error map prediction")
  Member "deep ordinal regression network for monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "self supervised sparse to dense self supervised depth completion from lidar and monocular camera":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "sparse and dense data with cnns depth completion and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "propagating confidences through cnns for sparse data regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "depth completion via deep basis fitting")

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)