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 improving the speed and accuracy of residual networks"
  Abstract: In this article, we take one step toward understanding the learning behavior of deep residual networks, and supporting the observation that deep residual networks behave like ensembles. We propose a new convolutional neural network architecture which builds upon the success of residual networks by explicitly exploiting the interpretation of very deep networks as an ensemble. The proposed multi-residual network increases the number of residual functions in the residual blocks. Our architecture generates models that are wider, rather than deeper, which significantly improves accuracy. We show that our model achieves an error rate of 3.73% and 19.45% on CIFAR-10 and CIFAR-100 respectively, that outperforms almost all of the existing models. We also demonstrate that our model outperforms very deep residual networks by 0.22% (top-1 error) on the full ImageNet 2012 classification dataset. Additionally, inspired by the parallel structure of multi-residual networks, a model parallelism technique has been investigated. The model parallelism method distributes the computation of residual blocks among the processors, yielding up to 15% computational complexity improvement.

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 "comb convolution for efficient convolutional architecture"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "log densenet how to sparsify a densenet"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "multi scale dense networks for resource efficient image classification"

Connected through hyperedge HE-3 (induced by source paper "identity connections in residual nets improve noise stability"):
  with members:
    - "norm preservation why residual networks can become extremely deep"
    - "skip connections eliminate singularities"
    - "the loss surface of residual networks ensembles and the role of batch normalization"
    - "residual connections encourage iterative inference"
    - "deep networks with stochastic depth"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "highway and residual networks learn unrolled iterative estimation"
    - "identity matters in deep learning"

Connected through hyperedge HE-4 (induced by source paper "the history began from alexnet a comprehensive survey on deep learning approaches"):
  with members:
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "an analysis of deep neural network models for practical applications"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "a deep convolutional neural network for background subtraction"
    - "a survey of neuromorphic computing and neural networks in hardware"

Connected through hyperedge HE-5 (induced by source paper "3d hand pose estimation using simulation and partial supervision with a shared latent space"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "cross modal deep variational hand pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "unsupervised image to image translation networks"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "towards good practices for deep 3d hand pose estimation"
    - "feature mapping for learning fast and accurate 3d pose inference from synthetic images"

Connected through hyperedge HE-6 (induced by source paper "face recognition with hybrid efficient convolution algorithms on fpgas"):
  with members:
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "an opencl tm deep learning accelerator on arria 10"
    - "hardware oriented approximation of convolutional neural networks"
    - "caffeinated fpgas fpga framework for convolutional neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "facenet a unified embedding for face recognition and clustering"
    - "fast algorithms for convolutional neural networks"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "deep convolutional neural network design patterns"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "weighted residuals for very deep 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-8 (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"

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)