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: "neural rgb d sensing depth and uncertainty from a video camera"
  Abstract: Depth sensing is crucial for 3D reconstruction and scene understanding. Active depth sensors provide dense metric measurements, but often suffer from limitations such as restricted operating ranges, low spatial resolution, sensor interference, and high power consumption. In this paper, we propose a deep learning (DL) method to estimate per-pixel depth and its uncertainty continuously from a monocular video stream, with the goal of effectively turning an RGB camera into an RGB-D camera. Unlike prior DL-based methods, we estimate a depth probability distribution for each pixel rather than a single depth value, leading to an estimate of a 3D depth probability volume for each input frame. These depth probability volumes are accumulated over time under a Bayesian filtering framework as more incoming frames are processed sequentially, which effectively reduces depth uncertainty and improves accuracy, robustness, and temporal stability. Compared to prior work, the proposed approach achieves more accurate and stable results, and generalizes better to new datasets. Experimental results also show the output of our approach can be directly fed into classical RGB-D based 3D scanning methods for 3D scene reconstruction.

Connected through hyperedge HE-1 (induced by source paper "uncertainty depth estimation with gated images for 3d reconstruction"):
  with members:
    - "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather"
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "learning single camera depth estimation using dual pixels"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "uncertainty estimation in one stage object detection"
    - "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data"
    - "gated2depth real time dense lidar from gated images"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"

Connected through hyperedge HE-2 (induced by source paper "scalable uncertainty for computer vision with functional variational inference"):
  with members:
    - "deepfactors real time probabilistic dense monocular slam"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "functional variational bayesian neural networks"
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "efficient uncertainty estimation for semantic segmentation in videos"

Connected through hyperedge HE-3 (induced by source paper "deepfactors real time probabilistic dense monocular slam"):
  with members:
    - "deeptam deep tracking and mapping"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "ls net learning to solve nonlinear least squares for monocular stereo"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "unsupervised learning of depth and ego motion from video"
    - "demon depth and motion network for learning monocular stereo"
    - "cnn slam real time dense monocular slam with learned depth prediction"

Connected through hyperedge HE-4 (induced by source paper "generating and exploiting probabilistic monocular depth estimates"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "estimating depth from rgb and sparse sensing"
    - "sparse to dense depth prediction from sparse depth samples and a single image"

Connected through hyperedge HE-5 (induced by source paper "point cloud instance segmentation using probabilistic embeddings"):
  with members:
    - "dynamic scale inference by entropy minimization"
    - "associatively segmenting instances and semantics in point clouds"
    - "3d instance segmentation via multi task metric learning"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "bag of tricks for image classification with convolutional neural networks"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"

Connected through hyperedge HE-6 (induced by source paper "video depth estimation by fusing flow to depth proposals"):
  with members:
    - "dpsnet end to end deep plane sweep stereo"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "deepmvs learning multi view stereopsis"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "deepv2d video to depth with differentiable structure from motion"
    - "mvsnet depth inference for unstructured multi view stereo"

Connected through hyperedge HE-7 (induced by source paper "tradi tracking deep neural network weight distributions"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "lca loss change allocation for neural network training"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "deep learning under privileged information using heteroscedastic dropout"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "ionet learning to cure the curse of drift in inertial odometry"

Connected through hyperedge HE-8 (induced by source paper "unsupervised monocular depth prediction for indoor continuous video streams"):
  with members:
    - "geometry aware symmetric domain adaptation for monocular depth estimation"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "ganvo unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks"
    - "digging into self supervised monocular depth estimation"
    - "depth prediction without the sensors leveraging structure for unsupervised learning from monocular videos"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"

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)