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: "interleaved group convolutions for deep neural networks"
  Abstract: In this paper, we present a simple and modularized neural network architecture, named interleaved group convolutional neural networks (IGCNets). The main point lies in a novel building block, a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution. The two group convolutions are complementary: (i) the convolution on each partition in primary group convolution is a spatial convolution, while on each partition in secondary group convolution, the convolution is a point-wise convolution; (ii) the channels in the same secondary partition come from different primary partitions. We discuss one representative advantage: Wider than a regular convolution with the number of parameters and the computation complexity preserved. We also show that regular convolutions, group convolution with summation fusion, and the Xception block are special cases of interleaved group convolutions. Empirical results over standard benchmarks, CIFAR-$10$, CIFAR-$100$, SVHN and ImageNet demonstrate that our networks are more efficient in using parameters and computation complexity with similar or higher accuracy.

Connected through hyperedge HE-1 (induced by source paper "predicting population neural activity in the algonauts challenge using end to end trained siamese networks and group convolutions"):
  with members:
    - "the algonauts project a platform for communication between the sciences of biological and artificial intelligence"
    - "cyclical learning rates for training neural networks"

Connected through hyperedge HE-2 (induced by source paper "esfnet efficient network for building extraction from high resolution aerial images"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "exfuse enhancing feature fusion for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "seesaw net convolution neural network with uneven group convolution"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "rethinking imagenet pre training"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "ai benchmark running deep neural networks on android smartphones"
    - "igcv 2 interleaved structured sparse convolutional neural networks"
    - "mnasnet platform aware neural architecture search for mobile"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-4 (induced by source paper "pydmobilenet improved version of mobilenets with pyramid depthwise separable convolution"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning transferable architectures for scalable image recognition"
    - "data distillation towards omni supervised learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mixup beyond empirical risk minimization"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "shufflenet v2 practical guidelines for efficient cnn architecture design"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "igcv 2 interleaved structured sparse convolutional neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "progressive neural architecture search"
    - "learning efficient convolutional networks through network slimming"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-6 (induced by source paper "competitive inner imaging squeeze and excitation for residual network"):
  with members:
    - "multi attention multi class constraint for fine grained image recognition"
    - "convolutional neural networks with alternately updated clique"
    - "learning what and where to attend"
    - "connectivity learning in multi branch networks"
    - "neural ordinary differential equations"
    - "cbam convolutional block attention module"
    - "skipnet learning dynamic routing in convolutional networks"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-7 (induced by source paper "quantization mimic towards very tiny cnn for object detection"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "recurrent scale approximation for object detection in cnn"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "channel pruning for accelerating very deep neural networks"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-8 (induced by source paper "efficient structured pruning and architecture searching for group convolution"):
  with members:
    - "fully learnable group convolution for acceleration of deep neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "building efficient deep neural networks with unitary group convolutions"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "extreme network compression via filter group approximation"
    - "mnasnet platform aware neural architecture search for mobile"
    - "rethinking the value of network pruning"

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)