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: "estimating depth from monocular images as classification using deep fully convolutional residual networks"
  Abstract: Depth estimation from single monocular images is a key component of scene understanding and has benefited largely from deep convolutional neural networks (CNN) recently. In this article, we take advantage of the recent deep residual networks and propose a simple yet effective approach to this problem. We formulate depth estimation as a pixel-wise classification task. Specifically, we first discretize the continuous depth values into multiple bins and label the bins according to their depth range. Then we train fully convolutional deep residual networks to predict the depth label of each pixel. Performing discrete depth label classification instead of continuous depth value regression allows us to predict a confidence in the form of probability distribution. We further apply fully-connected conditional random fields (CRF) as a post processing step to enforce local smoothness interactions, which improves the results. We evaluate our approach on both indoor and outdoor datasets and achieve state-of-the-art performance.

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 "single image depth estimation trained via depth from defocus cues"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth from defocus how can defocus blur improve 3d estimation using dense neural networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "dual attention network for scene segmentation"
    - "depth estimation using structured light flow analysis of projected pattern flow on an object s surface"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-3 (induced by source paper "diversedepth affine invariant depth prediction using diverse data"):
  with members:
    - "a large rgb d dataset for semi supervised monocular depth estimation"
    - "on the power of curriculum learning in training deep networks"
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "learning the depths of moving people by watching frozen people"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "how do neural networks see depth in single images"
    - "learning depth with convolutional spatial propagation network"
    - "deep ordinal regression network for monocular depth estimation"

Connected through hyperedge HE-4 (induced by source paper "close proximity underwater terrain mapping using learning based coarse range estimation"):
  with members:
    - "monocular depth estimation a survey"
    - "inferring distributions over depth from a single image"
    - "deep ordinal regression network for monocular depth estimation"
    - "bonnet an open source training and deployment framework for semantic segmentation in robotics using cnns"
    - "focal loss for dense object detection"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deeper depth prediction with fully convolutional residual networks"

Connected through hyperedge HE-5 (induced by source paper "structured knowledge distillation for dense prediction"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ocnet object context network for scene parsing"

Connected through hyperedge HE-6 (induced by source paper "auxiliary learning for deep multi task learning"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "ocnet object context network for scene parsing"
    - "deep ordinal regression network for monocular depth estimation"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "discrimination aware channel pruning for deep neural networks"

Connected through hyperedge HE-7 (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-8 (induced by source paper "learning depth from monocular videos using synthetic data a temporally consistent domain adaptation approach"):
  with members:
    - "learning the depths of moving people by watching frozen people"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "dual cnn models for unsupervised monocular depth estimation"

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)