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: "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"
  Abstract: In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth. Inspired by the indoor depth completion, our network estimates surface normals as the intermediate representation to produce dense depth, and can be trained end-to-end. With a modified encoder-decoder structure, our network effectively fuses the dense color image and the sparse LiDAR depth. To address outdoor specific challenges, our network predicts a confidence mask to handle mixed LiDAR signals near foreground boundaries due to occlusion, and combines estimates from the color image and surface normals with learned attention maps to improve the depth accuracy especially for distant areas. Extensive experiments demonstrate that our model improves upon the state-of-the-art performance on KITTI depth completion benchmark. Ablation study shows the positive impact of each model components to the final performance, and comprehensive analysis shows that our model generalizes well to the input with higher sparsity or from indoor scenes.

Connected through hyperedge HE-1 (induced by source paper "slimconv reducing channel redundancy in convolutional neural networks by weights flipping"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "res2net a new multi scale backbone architecture"
    - "feratt facial expression recognition with attention net"
    - "efficientdet scalable and efficient object detection"
    - "selective kernel networks"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "scene completenesss aware lidar depth completion for driving scenario"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "sdnet semantically guided depth estimation network"
    - "depth coefficients for depth completion"
    - "depth completion from sparse lidar data with depth normal constraints"
    - "3d lidar and stereo fusion using stereo matching network with conditional cost volume normalization"
    - "deep rgb d canonical correlation analysis for sparse depth completion"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "learning depth with convolutional spatial propagation network"

Connected through hyperedge HE-3 (induced by source paper "unsupervised enhancement of real world depth images using tri cycle gan"):
  with members:
    - "s flow gan"
    - "rethinking lossy compression the rate distortion perception tradeoff"
    - "generative adversarial networks for extreme learned image compression"
    - "deep depth completion of a single rgb d image"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "depth map completion by jointly exploiting blurry color images and sparse depth maps"
    - "sparse to dense depth prediction from sparse depth samples and a single image"

Connected through hyperedge HE-4 (induced by source paper "depth completion via deep basis fitting"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "depth coefficients for depth completion"
    - "dense depth posterior ddp from single image and sparse range"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "voiced depth completion from inertial odometry and vision"
    - "noise aware unsupervised deep lidar stereo fusion"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"

Connected through hyperedge HE-5 (induced by source paper "accurate monocular object detection via color embedded 3d reconstruction for autonomous driving"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep learning for generic object detection a survey"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "monogrnet a geometric reasoning network for monocular 3d object localization"
    - "roi 10d monocular lifting of 2d detection to 6d pose and metric shape"

Connected through hyperedge HE-6 (induced by source paper "cspn learning context and resource aware convolutional spatial propagation networks for depth completion"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "learning guided convolutional network for depth completion"
    - "deep high resolution representation learning for human pose estimation"
    - "learning depth with convolutional spatial propagation network"
    - "deep depth completion of a single rgb d image"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "structured attention guided convolutional neural fields for monocular depth estimation"

Connected through hyperedge HE-7 (induced by source paper "deep rgb d canonical correlation analysis for sparse depth completion"):
  with members:
    - "depth coefficients for depth completion"
    - "dfinenet ego motion estimation and depth refinement from sparse noisy depth input with rgb guidance"
    - "dense depth posterior ddp from single image and sparse range"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "learning depth with convolutional spatial propagation network"
    - "depth aware cnn for rgb d segmentation"
    - "deep depth completion of a single rgb d image"

Connected through hyperedge HE-8 (induced by source paper "learning guided convolutional network for depth completion"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "dense depth posterior ddp from single image and sparse range"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "learning depth with convolutional spatial propagation network"
    - "deep depth completion of a single rgb d image"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"

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)