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: "convolutional neural networks with alternately updated clique"
  Abstract: Improving information flow in deep networks helps to ease the training difficulties and utilize parameters more efficiently. Here we propose a new convolutional neural network architecture with alternately updated clique (CliqueNet). In contrast to prior networks, there are both forward and backward connections between any two layers in the same block. The layers are constructed as a loop and are updated alternately. The CliqueNet has some unique properties. For each layer, it is both the input and output of any other layer in the same block, so that the information flow among layers is maximized. During propagation, the newly updated layers are concatenated to re-update previously updated layer, and parameters are reused for multiple times. This recurrent feedback structure is able to bring higher level visual information back to refine low-level filters and achieve spatial attention. We analyze the features generated at different stages and observe that using refined features leads to a better result. We adopt a multi-scale feature strategy that effectively avoids the progressive growth of parameters. Experiments on image recognition datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet show that our proposed models achieve the state-of-the-art performance with fewer parameters.

Connected through hyperedge HE-1 (induced by source paper "lightweight residual densely connected convolutional neural network"):
  with members:
    - "denser deep evolutionary network structured representation"
    - "indoor scene understanding in 2 5 3d for autonomous agents a survey"
    - "residual dense network for image restoration"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "residual dense network for image super resolution"
    - "skipnet learning dynamic routing in convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-2 (induced by source paper "context gated convolution"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "attention augmented convolutional networks"
    - "generating long sequences with sparse transformers"
    - "weight standardization"
    - "eco efficient convolutional network for online video understanding"
    - "bam bottleneck attention module"

Connected through hyperedge HE-3 (induced by source paper "inner imaging networks put lenses into convolutional structure"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "channelnets compact and efficient convolutional neural networks via channel wise convolutions"
    - "training neural networks using features replay"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-4 (induced by source paper "stochastic region pooling make attention more expressive"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi attention multi class constraint for fine grained image recognition"
    - "harmonious attention network for person re identification"
    - "dropblock a regularization method for convolutional networks"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "learning what and where to attend"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-5 (induced by source paper "learning implicitly recurrent cnns through parameter sharing"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "neural arithmetic logic units"
    - "snas stochastic neural architecture search"
    - "sparsely aggregated convolutional networks"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "making neural programming architectures generalize via recursion"
    - "deep expander networks efficient deep networks from graph theory"

Connected through hyperedge HE-6 (induced by source paper "sognet scene overlap graph network for panoptic segmentation"):
  with members:
    - "learning instance occlusion for panoptic segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "an end to end network for panoptic segmentation"
    - "seamless scene segmentation"
    - "deeperlab single shot image parser"
    - "panoptic feature pyramid networks"
    - "bilinear attention networks"
    - "linknet relational embedding for scene graph"

Connected through hyperedge HE-7 (induced by source paper "fsd feature skyscraper detector for stem end and blossom end of navel orange"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "recent advances in object detection in the age of deep convolutional neural networks"
    - "deep learning for generic object detection a survey"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "yolov3 an incremental improvement"
    - "deep learning in agriculture a survey"
    - "fssd feature fusion single shot multibox detector"
    - "self normalizing neural networks"

Connected through hyperedge HE-8 (induced by source paper "fishnet a versatile backbone for image region and pixel level prediction"):
  with members:
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "i revnet deep invertible networks"
    - "question guided hybrid convolution for visual question answering"
    - "deep layer aggregation"
    - "learning feature pyramids for human pose estimation"
    - "dilated residual networks"
    - "multi scale dense networks for resource efficient image classification"
    - "squeeze and excitation networks"

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)