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: "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm"
  Abstract: Weight pruning and weight quantization are two important categories of DNN model compression. Prior work on these techniques are mainly based on heuristics. A recent work developed a systematic frame-work of DNN weight pruning using the advanced optimization technique ADMM (Alternating Direction Methods of Multipliers), achieving one of state-of-art in weight pruning results. In this work, we first extend such one-shot ADMM-based framework to guarantee solution feasibility and provide fast convergence rate, and generalize to weight quantization as well. We have further developed a multi-step, progressive DNN weight pruning and quantization framework, with dual benefits of (i) achieving further weight pruning/quantization thanks to the special property of ADMM regularization, and (ii) reducing the search space within each step. Extensive experimental results demonstrate the superior performance compared with prior work. Some highlights: (i) we achieve 246x,36x, and 8x weight pruning on LeNet-5, AlexNet, and ResNet-50 models, respectively, with (almost) zero accuracy loss; (ii) even a significant 61x weight pruning in AlexNet (ImageNet) results in only minor degradation in actual accuracy compared with prior work; (iii) we are among the first to derive notable weight pruning results for ResNet and MobileNet models; (iv) we derive the first lossless, fully binarized (for all layers) LeNet-5 for MNIST and VGG-16 for CIFAR-10; and (v) we derive the first fully binarized (for all layers) ResNet for ImageNet with reasonable accuracy loss.

Connected through hyperedge HE-1 (induced by source paper "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning"):
  with members:
    - "deep learning on mobile devices a review"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deepcache principled cache for mobile deep vision"
    - "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"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

Connected through hyperedge HE-2 (induced by source paper "learned threshold pruning"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "taxonomy and evaluation of structured compression of convolutional neural networks"
    - "mixconv mixed depthwise convolutional kernels"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "ai benchmark running deep neural networks on android smartphones"

Connected through hyperedge HE-3 (induced by source paper "active subspace of neural networks structural analysis and universal attacks"):
  with members:
    - "universal adversarial audio perturbations"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "universal adversarial perturbations for speech recognition systems"
    - "bayesian tensorized neural networks with automatic rank selection"
    - "mildly overparametrized neural nets can memorize training data efficiently"
    - "adversarial examples a complete characterisation of the phenomenon"
    - "gradient descent based d optimal design for the least squares polynomial approximation"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-4 (induced by source paper "an ultra efficient memristor based dnn framework with structured weight pruning and quantization using admm"):
  with members:
    - "26ms inference time for resnet 50 towards real time execution of all dnns on smartphone"
    - "toward extremely low bit and lossless accuracy in dnns with progressive admm"
    - "resnet can be pruned 60x introducing network purification and unused path removal p rm after weight pruning"
    - "an area and energy efficient design of domain wall memory based deep convolutional neural networks using stochastic computing"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "scalable noc based neuromorphic hardware learning and inference"
    - "group scissor scaling neuromorphic computing design to large neural networks"
    - "trannsformer n eural n etwork transform ation for memristive crossbar based neuromorphic system design"

Connected through hyperedge HE-5 (induced by source paper "26ms inference time for resnet 50 towards real time execution of all dnns on smartphone"):
  with members:
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "learning structured sparsity in deep neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "quantized convolutional neural networks for mobile devices"

Connected through hyperedge HE-6 (induced by source paper "resnet can be pruned 60x introducing network purification and unused path removal p rm after weight pruning"):
  with members:
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics"
    - "amc automl for model compression and acceleration on mobile devices"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "learning structured sparsity in deep neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)