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: "training cnns with low rank filters for efficient image classification"
  Abstract: We propose a new method for creating computationally efficient convolutional neural networks (CNNs) by using low-rank representations of convolutional filters. Rather than approximating filters in previously-trained networks with more efficient versions, we learn a set of small basis filters from scratch; during training, the network learns to combine these basis filters into more complex filters that are discriminative for image classification. To train such networks, a novel weight initialization scheme is used. This allows effective initialization of connection weights in convolutional layers composed of groups of differently-shaped filters. We validate our approach by applying it to several existing CNN architectures and training these networks from scratch using the CIFAR, ILSVRC and MIT Places datasets. Our results show similar or higher accuracy than conventional CNNs with much less compute. Applying our method to an improved version of VGG-11 network using global max-pooling, we achieve comparable validation accuracy using 41% less compute and only 24% of the original VGG-11 model parameters; another variant of our method gives a 1 percentage point increase in accuracy over our improved VGG-11 model, giving a top-5 center-crop validation accuracy of 89.7% while reducing computation by 16% relative to the original VGG-11 model. Applying our method to the GoogLeNet architecture for ILSVRC, we achieved comparable accuracy with 26% less compute and 41% fewer model parameters. Applying our method to a near state-of-the-art network for CIFAR, we achieved comparable accuracy with 46% less compute and 55% fewer parameters.

Connected through hyperedge HE-1 (induced by source paper "branched multi task networks deciding what layers to share"):
  with members:
    - "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "feature partitioning for efficient multi task architectures"
    - "attentive single tasking of multiple tasks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "evolutionary architecture search for deep multitask networks"
    - "regularized evolution for image classifier architecture search"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-2 (induced by source paper "provable filter pruning for efficient neural networks"):
  with members:
    - "revisiting hard thresholding for dnn pruning"
    - "sipping neural networks sensitivity informed provable pruning of neural networks"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "learning filter basis for convolutional neural network compression"
    - "the state of sparsity in deep neural networks"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "learning to prune filters in convolutional neural networks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"

Connected through hyperedge HE-3 (induced by source paper "hetconv heterogeneous kernel based convolutions for deep cnns"):
  with members:
    - "stability based filter pruning for accelerating deep cnns"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "differential attention for visual question answering"
    - "leveraging filter correlations for deep model compression"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "nisp pruning networks using neuron importance score propagation"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-4 (induced by source paper "dynamic multi path neural network"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "model compression via distillation and quantization"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "quantization mimic towards very tiny cnn for object detection"
    - "skipnet learning dynamic routing in convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-5 (induced by source paper "sipping neural networks sensitivity informed provable pruning of neural networks"):
  with members:
    - "revisiting hard thresholding for dnn pruning"
    - "on coresets for logistic regression"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "snip single shot network pruning based on connection sensitivity"
    - "rethinking the value of network pruning"
    - "training support vector machines using coresets"

Connected through hyperedge HE-6 (induced by source paper "gasl guided attention for sparsity learning in deep neural networks"):
  with members:
    - "attention is all you need"
    - "learning structured sparsity in deep neural networks"
    - "attention correctness in neural image captioning"
    - "neural machine translation with supervised attention"
    - "guided alignment training for topic aware neural machine translation"
    - "supervised attentions for neural machine translation"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-7 (induced by source paper "separable convolutional eigen filters scef building efficient cnns using redundancy analysis"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "network compression using correlation analysis of layer responses"
    - "extreme network compression via filter group approximation"
    - "nisp pruning networks using neuron importance score propagation"
    - "thinet a filter level pruning method for deep neural network compression"
    - "coordinating filters for faster deep neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "an entropy based pruning method for cnn compression"

Connected through hyperedge HE-8 (induced by source paper "recent advances in convolutional neural network acceleration"):
  with members:
    - "learning efficient convolutional networks through network slimming"
    - "structured probabilistic pruning for convolutional neural network acceleration"
    - "chain nn an energy efficient 1d chain architecture for accelerating deep convolutional neural networks"
    - "mec memory efficient convolution for deep neural network"
    - "coordinating filters for faster deep neural networks"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"
    - "channel pruning for accelerating very deep neural 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)