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: "depth from a single image by harmonizing overcomplete local network predictions"
  Abstract: A single color image can contain many cues informative towards different aspects of local geometric structure. We approach the problem of monocular depth estimation by using a neural network to produce a mid-level representation that summarizes these cues. This network is trained to characterize local scene geometry by predicting, at every image location, depth derivatives of different orders, orientations and scales. However, instead of a single estimate for each derivative, the network outputs probability distributions that allow it to express confidence about some coefficients, and ambiguity about others. Scene depth is then estimated by harmonizing this overcomplete set of network predictions, using a globalization procedure that finds a single consistent depth map that best matches all the local derivative distributions. We demonstrate the efficacy of this approach through evaluation on the NYU v2 depth data set.

Connected through hyperedge HE-1 (induced by source paper "cam convs camera aware multi scale convolutions for single view depth"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deeptam deep tracking and mapping"
    - "deep attention based classification network for robust depth prediction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "deepmvs learning multi view stereopsis"

Connected through hyperedge HE-2 (induced by source paper "exploiting temporal consistency for real time video depth estimation"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "rethinking monocular depth estimation with adversarial training"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "deepvs a deep learning based video saliency prediction approach"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"

Connected through hyperedge HE-3 (induced by source paper "peeking behind objects layered depth prediction from a single image"):
  with members:
    - "layer structured 3d scene inference via view synthesis"
    - "unsupervised learning of depth and ego motion from video"
    - "shape inpainting using 3d generative adversarial network and recurrent convolutional networks"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "factoring shape pose and layout from the 2d image of a 3d scene"
    - "semantic scene completion from a single depth image"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"

Connected through hyperedge HE-4 (induced by source paper "from big to small multi scale local planar guidance for monocular depth estimation"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "learning depth from monocular videos using direct methods"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "semi supervised deep learning for monocular depth map prediction"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-5 (induced by source paper "enforcing geometric constraints of virtual normal for depth prediction"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "fcos fully convolutional one stage object detection"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "rethinking monocular depth estimation with adversarial training"
    - "learning a discriminative feature network for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "visualization of convolutional neural networks for monocular depth estimation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep ordinal regression network for monocular depth estimation"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "evaluation of cnn based single image depth estimation methods"
    - "learn to pay attention"
    - "smoothgrad removing noise by adding noise"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-7 (induced by source paper "neural re simulation for generating bounces in single images"):
  with members:
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "neural rerendering in the wild"
    - "lookingood enhancing performance capture with real time neural re rendering"
    - "ignor image guided neural object rendering"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "learning to generate long term future via hierarchical prediction"
    - "learning to represent mechanics via long term extrapolation and interpolation"

Connected through hyperedge HE-8 (induced by source paper "generating and exploiting probabilistic monocular depth estimates"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "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"

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)