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: "weighted residuals for very deep networks"
  Abstract: Deep residual networks have recently shown appealing performance on many challenging computer vision tasks. However, the original residual structure still has some defects making it difficult to converge on very deep networks. In this paper, we introduce a weighted residual network to address the incompatibility between \texttt{ReLU} and element-wise addition and the deep network initialization problem. The weighted residual network is able to learn to combine residuals from different layers effectively and efficiently. The proposed models enjoy a consistent improvement over accuracy and convergence with increasing depths from 100+ layers to 1000+ layers. Besides, the weighted residual networks have little more computation and GPU memory burden than the original residual networks. The networks are optimized by projected stochastic gradient descent. Experiments on CIFAR-10 have shown that our algorithm has a \emph{faster convergence speed} than the original residual networks and reaches a \emph{high accuracy} at 95.3\% with a 1192-layer model.

Connected through hyperedge HE-1 (induced by source paper "residual networks of residual networks multilevel residual networks"):
  with members:
    - "resnet in resnet generalizing residual architectures"
    - "deep residual networks with exponential linear unit"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "convolutional residual memory networks"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "swapout learning an ensemble of deep architectures"

Connected through hyperedge HE-2 (induced by source paper "training deeper neural machine translation models with transparent attention"):
  with members:
    - "deep contextualized word representations"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "sharp models on dull hardware fast and accurate neural machine translation decoding on the cpu"
    - "massive exploration of neural machine translation architectures"
    - "convolutional sequence to sequence learning"
    - "deep neural machine translation with linear associative unit"
    - "deep architectures for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "data driven sparse structure selection for deep neural networks"):
  with members:
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "pelee a real time object detection system on mobile devices"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "large scale evolution of image classifiers"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-4 (induced by source paper "deep convolutional neural network design patterns"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "multi residual networks improving the speed and accuracy of residual networks"
    - "convolutional neural fabrics"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-5 (induced by source paper "deep pyramidal residual networks"):
  with members:
    - "deep networks with stochastic depth"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "swapout learning an ensemble of deep architectures"

Connected through hyperedge HE-6 (induced by source paper "voxresnet deep voxelwise residual networks for volumetric brain segmentation"):
  with members:
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "identity mappings in deep residual networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "wide residual networks"
    - "instance aware semantic segmentation via multi task network cascades"
    - "u net convolutional networks for biomedical image segmentation"
    - "parallel multi dimensional lstm with application to fast biomedical volumetric image 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)