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: "deep expander networks efficient deep networks from graph theory"
  Abstract: Deep Neural Networks, while being unreasonably effective for several vision tasks, have their usage limited by the computational and memory requirements, both during training and inference stages. Analyzing and improving the connectivity patterns between layers of a network has resulted in several compact architectures like GoogleNet, ResNet and DenseNet-BC. In this work, we utilize results from graph theory to develop an efficient connection pattern between consecutive layers. Specifically, we use {\it expander graphs} that have excellent connectivity properties to develop a sparse network architecture, the deep expander network (X-Net). The X-Nets are shown to have high connectivity for a given level of sparsity. We also develop highly efficient training and inference algorithms for such networks. Experimental results show that we can achieve the similar or better accuracy as DenseNet-BC with two-thirds the number of parameters and FLOPs on several image classification benchmarks. We hope that this work motivates other approaches to utilize results from graph theory to develop efficient network architectures.

Connected through hyperedge HE-1 (induced by source paper "neurofabric identifying ideal topologies for training a priori sparse networks"):
  with members:
    - "online object representations with contrastive learning"
    - "revisiting small batch training for deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "population based training of neural networks"
    - "learning structured sparsity in deep neural networks"
    - "orthogonal rnns and long memory tasks"

Connected through hyperedge HE-2 (induced by source paper "learning implicitly recurrent cnns through parameter sharing"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "pre defined sparse neural networks with hardware acceleration"):
  with members:
    - "characterizing sparse connectivity patterns in neural networks"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "recursive binary neural network learning model with 2 bit weight storage requirement"
    - "morse code datasets for machine learning"
    - "a highly parallel fpga implementation of sparse neural network training"
    - "revisiting small batch training for deep neural networks"
    - "accelerating training of deep neural networks via sparse edge processing"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-4 (induced by source paper "discovering neural wirings"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "sparse networks from scratch faster training without losing performance"
    - "learning sparse networks using targeted dropout"
    - "luck matters understanding training dynamics of deep relu networks"
    - "learning implicitly recurrent cnns through parameter sharing"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-5 (induced by source paper "radix net structured sparse matrices for deep neural networks"):
  with members:
    - "sparse deep neural network exact solutions"
    - "expressive power of recurrent neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the expressive power of neural networks a view from the width"
    - "enabling massive deep neural networks with the graphblas"
    - "training sparse neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "data free parameter pruning for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "training behavior of sparse neural network topologies"):
  with members:
    - "graphchallenge org raising the bar on graph analytic performance"
    - "exploring sparsity in recurrent neural networks"
    - "enabling massive deep neural networks with the graphblas"
    - "dynamic network surgery for efficient dnns"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "sparsely aggregated convolutional networks"):
  with members:
    - "log densenet how to sparsify a densenet"
    - "mixed link networks"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "the reversible residual network backpropagation without storing activations"
    - "deep layer aggregation"
    - "dual path networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-8 (induced by source paper "snip single shot network pruning based on connection sensitivity"):
  with members:
    - "exploring sparsity in recurrent neural networks"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "learning structured sparsity in deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "pruning convolutional neural networks for resource efficient inference"
    - "pruning filters for efficient convnets"
    - "dynamic network surgery for efficient dnns"
    - "compression of neural machine translation models via 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)