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: "dense transformer networks"
  Abstract: The key idea of current deep learning methods for dense prediction is to apply a model on a regular patch centered on each pixel to make pixel-wise predictions. These methods are limited in the sense that the patches are determined by network architecture instead of learned from data. In this work, we propose the dense transformer networks, which can learn the shapes and sizes of patches from data. The dense transformer networks employ an encoder-decoder architecture, and a pair of dense transformer modules are inserted into each of the encoder and decoder paths. The novelty of this work is that we provide technical solutions for learning the shapes and sizes of patches from data and efficiently restoring the spatial correspondence required for dense prediction. The proposed dense transformer modules are differentiable, thus the entire network can be trained. We apply the proposed networks on natural and biological image segmentation tasks and show superior performance is achieved in comparison to baseline methods.

Connected through hyperedge HE-1 (induced by source paper "fisheyedistancenet self supervised scale aware distance estimation using monocular fisheye camera for autonomous driving"):
  with members:
    - "woodscape a multi task multi camera fisheye dataset for autonomous driving"
    - "3d packing for self supervised monocular depth estimation"
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "rethinking imagenet pre training"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "group normalization"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "artistic style transfer for videos and spherical images"

Connected through hyperedge HE-2 (induced by source paper "restricted deformable convolution based road scene semantic segmentation using surround view cameras"):
  with members:
    - "rovo robust omnidirectional visual odometry for wide baseline wide fov camera systems"
    - "incorporating network built in priors in weakly supervised semantic segmentation"
    - "deformable convolutional networks"
    - "understanding convolution for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "decomposeme simplifying convnets for end to end learning"
    - "revisiting batch normalization for practical domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "ascnet adaptive scale convolutional neural networks for multi scale feature learning"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "automated segmentation of cervical nuclei in pap smear images using deformable multi path ensemble model"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "dilated residual networks"
    - "understanding convolution for semantic segmentation"
    - "single image super resolution with dilated convolution based multi scale information learning inception module"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"

Connected through hyperedge HE-4 (induced by source paper "learning to zoom a saliency based sampling layer for neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deformable convolutional networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the inaturalist species classification and detection dataset"
    - "dynamic computational time for visual attention"
    - "autoscaler scale attention networks for visual correspondence"
    - "eye tracking for everyone"
    - "attend infer repeat fast scene understanding with generative models"

Connected through hyperedge HE-5 (induced by source paper "statistical transformer networks learning shape and appearance models via self supervision"):
  with members:
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "convolutional neural network architecture for geometric matching"
    - "deformable convolutional networks"
    - "3d morphable models as spatial transformer networks"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "toward end to end face recognition through alignment learning"
    - "faceposenet making a case for landmark free face alignment"
    - "unsupervised learning of object landmarks by factorized spatial embeddings"

Connected through hyperedge HE-6 (induced by source paper "alignet partial shape agnostic alignment via unsupervised learning"):
  with members:
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "perceptual losses for real time style transfer and super resolution"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "improved stereo matching with constant highway networks and reflective confidence learning"
    - "shapenet an information rich 3d model repository"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "spatial transformer networks"

Connected through hyperedge HE-7 (induced by source paper "modeling 4d fmri data via spatio temporal convolutional neural networks st cnn"):
  with members:
    - "deformable convolutional networks"
    - "a survey on deep learning in medical image analysis"
    - "recurrent neural networks for spatiotemporal dynamics of intrinsic networks from fmri data"
    - "u net convolutional networks for biomedical image segmentation"
    - "fully convolutional networks for semantic 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)