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: "high quality monocular depth estimation via transfer learning"
  Abstract: Accurate depth estimation from images is a fundamental task in many applications including scene understanding and reconstruction. Existing solutions for depth estimation often produce blurry approximations of low resolution. This paper presents a convolutional neural network for computing a high-resolution depth map given a single RGB image with the help of transfer learning. Following a standard encoder-decoder architecture, we leverage features extracted using high performing pre-trained networks when initializing our encoder along with augmentation and training strategies that lead to more accurate results. We show how, even for a very simple decoder, our method is able to achieve detailed high-resolution depth maps. Our network, with fewer parameters and training iterations, outperforms state-of-the-art on two datasets and also produces qualitatively better results that capture object boundaries more faithfully. Code and corresponding pre-trained weights are made publicly available.

Connected through hyperedge HE-1 (induced by source paper "object detection on single monocular images through canonical correlation analysis"):
  with members:
    - "a survey of deep learning based object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "feature selective anchor free module for single shot object detection"
    - "deep rgb d canonical correlation analysis for sparse depth completion"
    - "end to end cross modality retrieval with cca projections and pairwise ranking loss"
    - "focal loss for dense object detection"
    - "relation networks for object detection"

Connected through hyperedge HE-2 (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-3 (induced by source paper "dfvs deep flow guided scene agnostic image based visual servoing"):
  with members:
    - "siamese convolutional neural network for sub millimeter accurate camera pose estimation and visual servoing"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "learning visual servoing with deep features and fitted q iteration"
    - "exploring convolutional networks for end to end visual servoing"
    - "sim2real view invariant visual servoing by recurrent control"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "low viewpoint forest depth dataset for sparse rover swarms"):
  with members:
    - "monocular depth estimation a survey"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "rgbd datasets past present and future"
    - "deeper depth prediction with fully convolutional residual networks"
    - "depth map prediction from a single image using a multi scale deep network"

Connected through hyperedge HE-5 (induced by source paper "refinedmpl refined monocular pseudolidar for 3d object detection in autonomous driving"):
  with members:
    - "from big to small multi scale local planar guidance for monocular depth estimation"
    - "panoptic segmentation"
    - "deep optics for monocular depth estimation and 3d object detection"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "disentangling monocular 3d object detection"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "monocular 3d object detection with pseudo lidar point cloud"

Connected through hyperedge HE-6 (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-7 (induced by source paper "cleargrasp 3d shape estimation of transparent objects for manipulation"):
  with members:
    - "sharpnet fast and accurate recovery of occluding contours in monocular depth estimation"
    - "the replica dataset a digital replica of indoor spaces"
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "tom net learning transparent object matting from a single image"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"

Connected through hyperedge HE-8 (induced by source paper "conf net toward high confidence dense 3d point cloud with error map prediction"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "generating and exploiting probabilistic monocular depth estimates"
    - "detail preserving depth estimation from a single image using attention guided networks"
    - "deep ordinal regression network for monocular depth estimation"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "propagating confidences through cnns for sparse data regression"

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)