human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "high resolution lidar based depth mapping using bilateral filter"
  Abstract: High resolution depth-maps, obtained by upsampling sparse range data from a 3D-LIDAR, find applications in many fields ranging from sensory perception to semantic segmentation and object detection. Upsampling is often based on combining data from a monocular camera to compensate the low-resolution of a LIDAR. This paper, on the other hand, introduces a novel framework to obtain dense depth-map solely from a single LIDAR point cloud; which is a research direction that has been barely explored. The formulation behind the proposed depth-mapping process relies on local spatial interpolation, using sliding-window (mask) technique, and on the Bilateral Filter (BF) where the variable of interest, the distance from the sensor, is considered in the interpolation problem. In particular, the BF is conveniently modified to perform depth-map upsampling such that the edges (foreground-background discontinuities) are better preserved by means of a proposed method which influences the range-based weighting term. Other methods for spatial upsampling are discussed, evaluated and compared in terms of different error measures. This paper also researches the role of the mask's size in the performance of the implemented methods. Quantitative and qualitative results from experiments on the KITTI Database, using LIDAR point clouds only, show very satisfactory performance of the approach introduced in this work.

Connected through hyperedge HE-1 (induced by source paper "region of interest segmentation from lidar point cloud for multirotor aerial vehicles"):
  with members:
    - "robust and efficient quadrotor trajectory generation for fast autonomous flight"
    - "ground profile recovery from aerial 3d lidar based maps"
    - "near real time data labeling using a depth sensor for emg based prosthetic arms"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"

Connected through hyperedge HE-2 (induced by source paper "dense depth posterior ddp from single image and sparse range"):
  with members:
    - "bilateral cyclic constraint and adaptive regularization for unsupervised monocular depth prediction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep depth completion of a single rgb d image"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sbnet sparse blocks network for fast inference"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "propagating confidences through cnns for sparse data regression"

Connected through hyperedge HE-3 (induced by source paper "deep active learning for efficient training of a lidar 3d object detector"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "cereals cost effective region based active learning for semantic segmentation"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "localization aware active learning for object detection"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"

Connected through hyperedge HE-4 (induced by source paper "deep learning assisted high resolution binocular stereo depth reconstruction"):
  with members:
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "real time dense depth estimation by fusing stereo with sparse depth measurements"
    - "superdepth self supervised super resolved monocular depth estimation"
    - "pyramid stereo matching network"
    - "anytime stereo image depth estimation on mobile devices"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "sparsity invariant cnns"

Connected through hyperedge HE-5 (induced by source paper "real time dense depth estimation by fusing stereo with sparse depth measurements"):
  with members:
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "sparsity invariant cnns"
    - "embedded real time stereo estimation via semi global matching on the gpu"
    - "stereo matching by training a convolutional neural network to compare image patches"

Connected through hyperedge HE-6 (induced by source paper "motion guided lidar camera autocalibration and accelerated depth super resolution"):
  with members:
    - "online convolutional dictionary learning for multimodal imaging"
    - "stereo matching by training a convolutional neural network to compare image patches"

Question: Please tell me which class the center node 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)