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: "network trimming a data driven neuron pruning approach towards efficient deep architectures"
  Abstract: State-of-the-art neural networks are getting deeper and wider. While their performance increases with the increasing number of layers and neurons, it is crucial to design an efficient deep architecture in order to reduce computational and memory costs. Designing an efficient neural network, however, is labor intensive requiring many experiments, and fine-tunings. In this paper, we introduce network trimming which iteratively optimizes the network by pruning unimportant neurons based on analysis of their outputs on a large dataset. Our algorithm is inspired by an observation that the outputs of a significant portion of neurons in a large network are mostly zero, regardless of what inputs the network received. These zero activation neurons are redundant, and can be removed without affecting the overall accuracy of the network. After pruning the zero activation neurons, we retrain the network using the weights before pruning as initialization. We alternate the pruning and retraining to further reduce zero activations in a network. Our experiments on the LeNet and VGG-16 show that we can achieve high compression ratio of parameters without losing or even achieving higher accuracy than the original network.

Connected through hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression"):
  with members:
    - "hierarchical binary cnns for landmark localization with limited resources"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "gate decorator global filter pruning method for accelerating deep convolutional neural networks"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "importance estimation for neural network pruning"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-2 (induced by source paper "a network pruning network approach to deep model compression"):
  with members:
    - "cooperative initialization based deep neural network training"
    - "accuracy booster performance boosting using feature map re calibration"
    - "cpwc contextual point wise convolution for object recognition"
    - "play and prune adaptive filter pruning for deep model compression"
    - "stability based filter pruning for accelerating deep cnns"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "amc automl for model compression and acceleration on mobile devices"
    - "leveraging filter correlations for deep model compression"

Connected through hyperedge HE-3 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "bigdl a distributed deep learning framework for big data"
    - "network decoupling from regular to depthwise separable convolutions"
    - "deep neural network compression with single and multiple level quantization"

Connected through hyperedge HE-4 (induced by source paper "progressive gradient pruning for classification detection and domainadaptation"):
  with members:
    - "importance estimation for neural network pruning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "structured pruning of neural networks with budget aware regularization"
    - "yolov3 an incremental improvement"
    - "sbnet sparse blocks network for fast inference"
    - "discrimination aware channel pruning for deep neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

Connected through hyperedge HE-5 (induced by source paper "sasl saliency adaptive sparsity learning for neural network acceleration"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "oicsr out in channel sparsity regularization for compact deep neural networks"
    - "importance estimation for neural network pruning"
    - "global sparse momentum sgd for pruning very deep neural networks"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "learning to prune filters in convolutional neural networks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "structured pruning of neural networks with budget aware regularization"

Connected through hyperedge HE-6 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way"):
  with members:
    - "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "wide activation for efficient and accurate image super resolution"
    - "multi style generative network for real time transfer"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-7 (induced by source paper "binaryduo reducing gradient mismatch in binary activation network by coupling binary activations"):
  with members:
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "regularizing activation distribution for training binarized deep networks"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "matrix and tensor decompositions for training binary neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"

Connected through hyperedge HE-8 (induced by source paper "hrank filter pruning using high rank feature map"):
  with members:
    - "filter sketch for network pruning"
    - "channel pruning via automatic structure search"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "revisiting the importance of individual units in cnns via ablation"
    - "recent advances in efficient computation of deep convolutional neural networks"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"

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.NE(Neural and Evolutionary Computing)