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: "digging into self supervised monocular depth estimation"
  Abstract: Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this limitation, self-supervised learning has emerged as a promising alternative for training models to perform monocular depth estimation. In this paper, we propose a set of improvements, which together result in both quantitatively and qualitatively improved depth maps compared to competing self-supervised methods. #R##N#Research on self-supervised monocular training usually explores increasingly complex architectures, loss functions, and image formation models, all of which have recently helped to close the gap with fully-supervised methods. We show that a surprisingly simple model, and associated design choices, lead to superior predictions. In particular, we propose (i) a minimum reprojection loss, designed to robustly handle occlusions, (ii) a full-resolution multi-scale sampling method that reduces visual artifacts, and (iii) an auto-masking loss to ignore training pixels that violate camera motion assumptions. We demonstrate the effectiveness of each component in isolation, and show high quality, state-of-the-art results on the KITTI benchmark.

Connected through hyperedge HE-1 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation"):
  with members:
    - "modularized textual grounding for counterfactual resilience"
    - "geometry aware symmetric domain adaptation for monocular depth estimation"
    - "learning to adapt structured output space for semantic segmentation"
    - "dropblock a regularization method for convolutional networks"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "deep ordinal regression network for monocular depth estimation"
    - "taskonomy disentangling task transfer learning"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"

Connected through hyperedge HE-2 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry"):
  with members:
    - "visual inertial mapping with non linear factor recovery"
    - "panoptic segmentation"
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "multi frame gan image enhancement for stereo visual odometry in low light"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "visual odometry revisited what should be learnt"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"

Connected through hyperedge HE-3 (induced by source paper "don t forget the past recurrent depth estimation from monocular video"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"
    - "dynamonet dynamic action and motion network"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "depth estimation via affinity learned with convolutional spatial propagation network"

Connected through hyperedge HE-4 (induced by source paper "gated fusion network for degraded image super resolution"):
  with members:
    - "gated fusion network for single image dehazing"
    - "image super resolution using very deep residual channel attention networks"
    - "scale recurrent network for deep image deblurring"
    - "yolov3 an incremental improvement"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "density aware single image de raining using a multi stream dense network"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-5 (induced by source paper "semantically guided representation learning for self supervised monocular depth"):
  with members:
    - "sdnet semantically guided depth estimation network"
    - "3d packing for self supervised monocular depth estimation"
    - "upsnet a unified panoptic segmentation network"
    - "seamless scene segmentation"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "panoptic feature pyramid networks"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "group normalization"

Connected through hyperedge HE-6 (induced by source paper "monolayout amodal scene layout from a single image"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "wise ale wide sample estimator for approximate latent embedding"
    - "monocular semantic occupancy grid mapping with convolutional variational encoder decoder networks"
    - "pixor real time 3d object detection from point clouds"
    - "learning 2d to 3d lifting for object detection in 3d for autonomous vehicles"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "learning to look around objects for top view representations of outdoor scenes"

Connected through hyperedge HE-7 (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-8 (induced by source paper "semantic object prediction and spatial sound super resolution with binaural sounds"):
  with members:
    - "self supervised moving vehicle tracking with stereo sound"
    - "the sound of motions"
    - "co separating sounds of visual objects"
    - "scene aware audio for 360 videos"
    - "learning to localize sound source in visual scenes"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "learning to separate object sounds by watching unlabeled video"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

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)