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: "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"
  Abstract: Monocular depth estimation is a challenging task in complex compositions depicting multiple objects of diverse scales. Albeit the recent great progress thanks to the deep convolutional neural networks (CNNs), the state-of-the-art monocular depth estimation methods still fall short to handle such real-world challenging scenarios. In this paper, we propose a deep end-to-end learning framework to tackle these challenges, which learns the direct mapping from a color image to the corresponding depth map. First, we represent monocular depth estimation as a multi-category dense labeling task by contrast to the regression based formulation. In this way, we could build upon the recent progress in dense labeling such as semantic segmentation. Second, we fuse different side-outputs from our front-end dilated convolutional neural network in a hierarchical way to exploit the multi-scale depth cues for depth estimation, which is critical to achieve scale-aware depth estimation. Third, we propose to utilize soft-weighted-sum inference instead of the hard-max inference, transforming the discretized depth score to continuous depth value. Thus, we reduce the influence of quantization error and improve the robustness of our method. Extensive experiments on the NYU Depth V2 and KITTI datasets show the superiority of our method compared with current state-of-the-art methods. Furthermore, experiments on the NYU V2 dataset reveal that our model is able to learn the probability distribution of depth.

Connected through hyperedge HE-1 (induced by source paper "dipe deeper into photometric errors for unsupervised learning of depth and ego motion from monocular videos"):
  with members:
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "superdepth self supervised super resolved monocular depth estimation"

Connected through hyperedge HE-2 (induced by source paper "multidepth single image depth estimation via multi task regression and classification"):
  with members:
    - "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"
    - "deep attention based classification network for robust depth prediction"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "depth estimation via affinity learned with convolutional spatial propagation network"

Connected through hyperedge HE-3 (induced by source paper "attention based context aggregation network for monocular depth estimation"):
  with members:
    - "rethinking monocular depth estimation with adversarial training"
    - "deep multi scale architectures for monocular depth estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "complex yolo real time 3d object detection on point clouds"
    - "dilated residual networks"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "understanding convolution for semantic segmentation"
    - "cnn slam real time dense monocular slam with learned depth prediction"

Connected through hyperedge HE-4 (induced by source paper "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "deep attention based classification network for robust depth prediction"
    - "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"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "sparse and dense data with cnns depth completion and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "task aware monocular depth estimation for 3d object detection"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "towards instance level image to image translation"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "geometry aware symmetric domain adaptation for monocular depth estimation"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
    - "deep attention based classification network for robust depth prediction"

Connected through hyperedge HE-6 (induced by source paper "learn stereo infer mono siamese networks for self supervised monocular depth estimation"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "digging into self supervised monocular depth estimation"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "extreme 3d face reconstruction seeing through occlusions"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-7 (induced by source paper "student becoming the master knowledge amalgamation for joint scene parsing depth estimation and more"):
  with members:
    - "amalgamating knowledge towards comprehensive classification"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "knowledge concentration learning 100k object classifiers in a single cnn"
    - "an adversarial regularisation for semi supervised training of structured output neural networks"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "semi and weakly supervised semantic segmentation using generative adversarial network"
    - "joint estimation of camera pose depth deblurring and super resolution from a blurred image sequence"
    - "mask r cnn"

Connected through hyperedge HE-8 (induced by source paper "sdnet semantically guided depth estimation network"):
  with members:
    - "deep attention based classification network for robust depth prediction"
    - "group normalization"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "learning monocular depth by distilling cross domain stereo networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"

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)