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: "adam admm a unified systematic framework of structured weight pruning for dnns"
  Abstract: Weight pruning methods of deep neural networks have been demonstrated to achieve a good model pruning ratio without loss of accuracy, thereby alleviating the significant computation/storage requirements of large-scale DNNs. Structured weight pruning methods have been proposed to overcome the limitation of irregular network structure and demonstrated actual GPU acceleration. However, the pruning ratio and GPU acceleration are limited when accuracy needs to be maintained. In this work, we overcome pruning ratio and GPU acceleration limitations by proposing a unified, systematic framework of structured weight pruning for DNNs, named ADAM-ADMM. It is a framework that can be used to induce different types of structured sparsity, such as filter-wise, channel-wise, and shape-wise sparsity, as well non-structured sparsity. The proposed framework incorporates stochastic gradient descent with ADMM, and can be understood as a dynamic regularization method in which the regularization target is analytically updated in each iteration. A significant improvement in structured weight pruning ratio is achieved without loss of accuracy, along with fast convergence rate. With a small sparsity degree of 33.3% on the convolutional layers, we achieve 1.64% accuracy enhancement for the AlexNet model. This is obtained by mitigation of overfitting. Without loss of accuracy on the AlexNet model, we achieve 2.58x and 3.65x average measured speedup on two GPUs, clearly outperforming the prior work. The average speedups reach 2.77x and 7.5x when allowing a moderate accuracy loss of 2%. In this case the model compression for convolutional layers is 13.2x, corresponding to 10.5x CPU speedup. Our experiments on ResNet model and on other datasets like UCF101 and CIFAR-10 demonstrate the consistently higher performance of our framework. Our models and codes are released at this https URL

Connected through hyperedge HE-1 (induced by source paper "a privacy preserving dnn pruning and mobile acceleration framework"):
  with members:
    - "asymptotic soft filter pruning for deep convolutional neural networks"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "data free learning of student networks"
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "amc automl for model compression and acceleration on mobile devices"
    - "private model compression via knowledge distillation"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

Connected through hyperedge HE-2 (induced by source paper "ss auto a single shot automatic structured weight pruning framework of dnns with ultra high efficiency"):
  with members:
    - "non structured dnn weight pruning considered harmful"
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "amc automl for model compression and acceleration on mobile devices"
    - "discrimination aware channel pruning for deep neural networks"
    - "systematic weight pruning of dnns using alternating direction method of multipliers"
    - "rethinking the value of network pruning"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-3 (induced by source paper "blk rew a unified block based dnn pruning framework using reweighted regularization method"):
  with members:
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "amc automl for model compression and acceleration on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "e rnn design optimization for efficient recurrent neural networks in fpgas"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-4 (induced by source paper "adversarial robustness vs model compression or both"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "a direct approach to robust deep learning using adversarial networks"
    - "defensive quantization when efficiency meets robustness"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "sparse dnns with improved adversarial robustness"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-5 (induced by source paper "scann synthesis of compact and accurate neural networks"):
  with members:
    - "hardware guided symbiotic training for compact accurate yet execution efficient lstm"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "grow and prune compact fast and accurate lstms"
    - "mnasnet platform aware neural architecture search for mobile"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning transferable architectures for scalable image recognition"
    - "shift a zero flop zero parameter alternative to spatial convolutions"

Connected through hyperedge HE-6 (induced by source paper "a programmable approach to model compression"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "progressive deep neural networks acceleration via soft filter pruning"
    - "attention is all you need"
    - "model compression as constrained optimization with application to neural nets part i general framework"
    - "thinet a filter level pruning method for deep neural network compression"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-7 (induced by source paper "comprehensive snn compression using admm optimization and activity regularization"):
  with members:
    - "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "training and inference with integers in deep neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "progressive weight pruning of deep neural networks using admm"
    - "spiking deep residual network"

Connected through hyperedge HE-8 (induced by source paper "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm"):
  with members:
    - "demystifying neural network filter pruning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "differentiable fine grained quantization for deep neural network compression"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"

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)