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: "wider or deeper revisiting the resnet model for visual recognition"
  Abstract: The trend towards increasingly deep neural networks has been driven by a general observation that increasing depth increases the performance of a network. Recently, however, evidence has been amassing that simply increasing depth may not be the best way to increase performance, particularly given other limitations. Investigations into deep residual networks have also suggested that they may not in fact be operating as a single deep network, but rather as an ensemble of many relatively shallow networks. We examine these issues, and in doing so arrive at a new interpretation of the unravelled view of deep residual networks which explains some of the behaviours that have been observed experimentally. As a result, we are able to derive a new, shallower, architecture of residual networks which significantly outperforms much deeper models such as ResNet-200 on the ImageNet classification dataset. We also show that this performance is transferable to other problem domains by developing a semantic segmentation approach which outperforms the state-of-the-art by a remarkable margin on datasets including PASCAL VOC, PASCAL Context, and Cityscapes. The architecture that we propose thus outperforms its comparators, including very deep ResNets, and yet is more efficient in memory use and sometimes also in training time. The code and models are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "key instance selection for unsupervised video object segmentation"
    - "large scale object mining for object discovery from unlabeled video"
    - "an efficient 3d cnn for action object segmentation in video"
    - "video instance segmentation"
    - "video object segmentation using space time memory networks"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "dmm net differentiable mask matching network for video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "attention guided chained context aggregation for semantic segmentation"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "selective kernel networks"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "real time high performance semantic image segmentation of urban street scenes"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "lednet a lightweight encoder decoder network for real time semantic segmentation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "vortex pooling improving context representation in semantic segmentation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "yolov3 an incremental improvement"
    - "light weight refinenet for real time semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "high order paired aspp networks for semantic segmenation"):
  with members:
    - "asymmetric non local neural networks for semantic segmentation"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "mixed high order attention network for person re identification"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "alignseg feature aligned segmentation networks"):
  with members:
    - "gated scnn gated shape cnns for semantic segmentation"
    - "indices matter learning to index for deep image matting"
    - "adaptive context network for scene parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "guided upsampling network for real time semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "deep neural network perception models and robust autonomous driving systems"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "one pixel attack for fooling deep neural networks"
    - "self driving cars a survey"
    - "adversarially robust distillation"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "a survey of autonomous driving common practices and emerging technologies"
    - "panoptic deeplab a simple strong and fast baseline for bottom up panoptic segmentation"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "refuge challenge a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs"):
  with members:
    - "patch based output space adversarial learning for joint optic disc and cup segmentation"
    - "classification of findings with localized lesions in fundoscopic images using a regionally guided cnn"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "why rankings of biomedical image analysis competitions should be interpreted with care"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "deep learning to assess glaucoma risk and associated features in fundus images"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-8 (induced by source paper "concurrently extrapolating and interpolating networks for continuous model generation"):
  with members:
    - "bi directional cascade network for perceptual edge detection"
    - "cfsnet toward a controllable feature space for image restoration"
    - "pixel adaptive convolutional neural networks"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "image super resolution by neural texture transfer"
    - "a general decoupled learning framework for parameterized image operators"
    - "a benchmark for edge preserving image smoothing"

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)