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: "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
  Abstract: Model pruning has become a useful technique that improves the computational efficiency of deep learning, making it possible to deploy solutions on resource-limited scenarios. A widely-used practice in relevant work assumes that a smaller-norm parameter or feature plays a less informative role at the inference time. In this paper, we propose a channel pruning technique for accelerating the computations of deep convolutional neural networks (CNNs), which does not critically rely on this assumption. Instead, it focuses on direct simplification of the channel-to-channel computation graph of a CNN without the need of performing a computational difficult and not always useful task of making high-dimensional tensors of CNN structured sparse. Our approach takes two stages: the first being to adopt an end-to-end stochastic training method that eventually forces the outputs of some channels being constant, and the second being to prune those constant channels from the original neural network by adjusting the biases of their impacting layers such that the resulting compact model can be quickly fine-tuned. Our approach is mathematically appealing from an optimization perspective and easy to reproduce. We experimented our approach through several image learning benchmarks and demonstrate its interesting aspects and the competitive performance.

Connected through hyperedge HE-1 (induced by source paper "towards efficient model compression via learned global ranking"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "regularizing activation distribution for training binarized deep networks"
    - "adascale towards real time video object detection using adaptive scaling"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "importance estimation for neural network pruning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mavbench micro aerial vehicle benchmarking"

Connected through hyperedge HE-2 (induced by source paper "model agnostic structured sparsification with learnable channel shuffle"):
  with members:
    - "search to distill pearls are everywhere but not the eyes"
    - "differentiable learning to group channels via groupable convolutional neural networks"
    - "importance estimation for neural network pruning"
    - "fully learnable group convolution for acceleration of deep neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-3 (induced by source paper "spatially adaptive inference with stochastic feature sampling and interpolation"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "importance estimation for neural network pruning"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "sbnet sparse blocks network for fast inference"
    - "an end to end approach for speeding up neural network inference"
    - "deformable convnets v2 more deformable better results"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "filter grafting for deep neural networks"):
  with members:
    - "omni scale feature learning for person re identification"
    - "importance estimation for neural network pruning"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "network transplanting"
    - "scsp spectral clustering filter pruning with soft self adaption manners"
    - "network compression using correlation analysis of layer responses"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "structured compression by weight encryption for unstructured pruning and quantization"):
  with members:
    - "network pruning for low rank binary indexing"
    - "alternating multi bit quantization for recurrent neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "retraining based iterative weight quantization for deep neural networks"
    - "deeptwist learning model compression via occasional weight distortion"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "channel pruning for accelerating very deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-6 (induced by source paper "knapsack pruning with inner distillation"):
  with members:
    - "network pruning via transformable architecture search"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "contrastive representation distillation"
    - "importance estimation for neural network pruning"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "amc automl for model compression and acceleration on mobile devices"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "gradual channel pruning while training using feature relevance scores for convolutional neural networks"):
  with members:
    - "really should we pruning after model be totally trained pruning based on a small amount of training"
    - "discrimination aware channel pruning for deep neural networks"
    - "repr improved training of convolutional filters"
    - "rethinking the value of network pruning"
    - "nisp pruning networks using neuron importance score propagation"
    - "thinet a filter level pruning method for deep neural network compression"
    - "more is less a more complicated network with less inference complexity"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "autoslim towards one shot architecture search for channel numbers"):
  with members:
    - "universally slimmable networks and improved training techniques"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "learning to prune filters in convolutional neural networks"
    - "slimmable neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "graph hypernetworks for neural architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"

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.LG(Machine Learning)