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: "multi residual networks"
  Abstract: In this article, we take one step toward understanding the learning behavior of deep residual networks, and supporting the hypothesis that deep residual networks are exponential ensembles by construction. We examine the effective range of ensembles by introducing multi-residual networks that significantly improve classification accuracy of residual networks. The multi-residual networks increase the number of residual functions in the residual blocks. This is shown to improve the accuracy of the residual network when the network is deeper than a threshold. Based on a series of empirical studies on CIFAR-10 and CIFAR-100 datasets, the proposed multi-residual network yield $6\%$ and $10\%$ improvement with respect to the residual networks with identity mappings. Comparing with other state-of-the-art models, the proposed multi-residual network obtains a test error rate of $3.92\%$ on CIFAR-10 that outperforms all existing models.

Connected through hyperedge HE-1 (induced by source paper "selective kernel networks"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "bam bottleneck attention module"
    - "residual networks of residual networks multilevel residual networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image super resolution using very deep residual channel attention networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "person search via a mask guided two stream cnn model"
    - "igcv 2 interleaved structured sparse convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "blockdrop dynamic inference paths in residual networks"):
  with members:
    - "nisp pruning networks using neuron importance score propagation"
    - "data driven sparse structure selection for deep neural networks"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "training quantized nets a deeper understanding"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "multi scale dense networks for resource efficient image classification"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-3 (induced by source paper "interleaved group convolutions for deep neural networks"):
  with members:
    - "the power of sparsity in convolutional neural networks"
    - "resnet in resnet generalizing residual architectures"
    - "aggregated residual transformations for deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "deep convolutional neural networks with merge and run mappings"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "deep convolutional neural network design patterns"
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "resnet in resnet generalizing residual architectures"
    - "sgdr stochastic gradient descent with restarts"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-5 (induced by source paper "polynet a pursuit of structural diversity in very deep networks"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "multi residual networks improving the speed and accuracy of residual networks"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual 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)