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: "dual cnn models for unsupervised monocular depth estimation"
  Abstract: A lot of progress has been made to solve the depth estimation problem in stereo vision. Though, a very satisfactory performance is observed by utilizing the deep learning in supervised manner for depth estimation. This approach needs huge amount of ground truth training data as well as depth maps which is very laborious to prepare and many times it is not available in real scenario. Thus, the unsupervised depth estimation is the recent trend by utilizing the binocular stereo images to get rid of depth map ground truth. In unsupervised depth computation, the disparity images are generated by training the CNN with an image reconstruction loss based on the epipolar geometry constraints. The effective way of using CNN as well as investigating the better losses for the said problem needs to be addressed. In this paper, a dual CNN based model is presented for unsupervised depth estimation with 6 losses (DNM6) with individual CNN for each view to generate the corresponding disparity map. The proposed dual CNN model is also extended with 12 losses (DNM12) by utilizing the cross disparities. The presented DNM6 and DNM12 models are experimented over KITTI driving and Cityscapes urban database and compared with the recent state-of-the-art result of unsupervised depth estimation.

Connected through hyperedge HE-1 (induced by source paper "learning hyperspectral feature extraction and classification with resnext network"):
  with members:
    - "lisht non parametric linearly scaled hyperbolic tangent activation function for neural networks"
    - "3 d deep learning approach for remote sensing image classification"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "rccnet an efficient convolutional neural network for histological routine colon cancer nuclei classification"
    - "mask r cnn"
    - "aggregated residual transformations for deep neural networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (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"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "geometry aware symmetric domain adaptation for monocular depth estimation"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "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"
    - "learning depth from single images with deep neural network embedding focal length"

Connected through hyperedge HE-4 (induced by source paper "lisht non parametric linearly scaled hyperbolic tangent activation function for neural networks"):
  with members:
    - "average biased relu based cnn descriptor for improved face retrieval"
    - "complex valued neural networks with non parametric activation functions"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "rccnet an efficient convolutional neural network for histological routine colon cancer nuclei classification"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "dfunet convolutional neural networks for diabetic foot ulcer classification"
    - "visualizing the loss landscape of neural nets"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "hybridsn exploring 3d 2d cnn feature hierarchy for hyperspectral image classification"):
  with members:
    - "lisht non parametric linearly scaled hyperbolic tangent activation function for neural networks"
    - "3 d deep learning approach for remote sensing image classification"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "mask r cnn"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"

Connected through hyperedge HE-6 (induced by source paper "a performance evaluation of convolutional neural networks for face anti spoofing"):
  with members:
    - "a performance comparison of loss functions for deep face recognition"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "mask r cnn"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "semantic segmentation of colon glands with deep convolutional neural networks and total variation segmentation"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "deep representations for iris face and fingerprint spoofing detection"

Connected through hyperedge HE-7 (induced by source paper "rccnet an efficient convolutional neural network for histological routine colon cancer nuclei classification"):
  with members:
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "a survey on deep learning in medical image analysis"
    - "wide residual networks"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

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)