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: "a unified framework of dnn weight pruning and weight clustering quantization using admm"
  Abstract: Many model compression techniques of Deep Neural Networks (DNNs) have been investigated, including weight pruning, weight clustering and quantization, etc. Weight pruning leverages the redundancy in the number of weights in DNNs, while weight clustering/quantization leverages the redundancy in the number of bit representations of weights. They can be effectively combined in order to exploit the maximum degree of redundancy. However, there lacks a systematic investigation in literature towards this direction. #R##N#In this paper, we fill this void and develop a unified, systematic framework of DNN weight pruning and clustering/quantization using Alternating Direction Method of Multipliers (ADMM), a powerful technique in optimization theory to deal with non-convex optimization problems. Both DNN weight pruning and clustering/quantization, as well as their combinations, can be solved in a unified manner. For further performance improvement in this framework, we adopt multiple techniques including iterative weight quantization and retraining, joint weight clustering training and centroid updating, weight clustering retraining, etc. The proposed framework achieves significant improvements both in individual weight pruning and clustering/quantization problems, as well as their combinations. For weight pruning alone, we achieve 167x weight reduction in LeNet-5, 24.7x in AlexNet, and 23.4x in VGGNet, without any accuracy loss. For the combination of DNN weight pruning and clustering/quantization, we achieve 1,910x and 210x storage reduction of weight data on LeNet-5 and AlexNet, respectively, without accuracy loss. Our codes and models are released at the link this http URL

Connected through hyperedge HE-1 (induced by source paper "gradient based deep quantization of neural networks through sinusoidal adaptive regularization"):
  with members:
    - "divide and conquer leveraging intermediate feature representations for quantized training of neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "mixed precision dnns all you need is a good parametrization"
    - "sinreq generalized sinusoidal regularization for low bitwidth deep quantized training"
    - "learned step size quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning low precision deep neural networks through regularization"
    - "loss aware weight quantization of deep networks"

Connected through hyperedge HE-2 (induced by source paper "adversarially trained model compression when robustness meets efficiency"):
  with members:
    - "learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization"
    - "adversarial robustness may be at odds with simplicity"
    - "defensive quantization when efficiency meets robustness"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sparse dnns with improved adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"

Connected through hyperedge HE-3 (induced by source paper "model compression with adversarial robustness a unified optimization framework"):
  with members:
    - "learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization"
    - "adversarial robustness may be at odds with simplicity"
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "defensive quantization when efficiency meets robustness"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "sparse dnns with improved adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"

Connected through hyperedge HE-4 (induced by source paper "automatic neural network compression by sparsity quantization joint learning a constrained optimization based approach"):
  with members:
    - "single path one shot neural architecture search with uniform sampling"
    - "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"
    - "ecc energy constrained deep neural network compression via a bilinear regression model"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "energy constrained compression for deep neural networks via weighted sparse projection and layer input masking"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "ecc platform independent energy constrained deep neural network compression via a bilinear regression model"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "amc automl for model compression and acceleration on mobile devices"
    - "energy constrained compression for deep neural networks via weighted sparse projection and layer input masking"
    - "compressing neural networks using the variational information bottleneck"
    - "learning efficient convolutional networks through network slimming"
    - "self normalizing neural networks"
    - "towards closing the energy gap between hog and cnn features for embedded vision"

Connected through hyperedge HE-6 (induced by source paper "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"):
  with members:
    - "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"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "finn a framework for fast scalable binarized neural network inference"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "releq an automatic reinforcement learning approach for deep quantization of neural networks"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "tvm end to end optimization stack for deep learning"
    - "haq hardware aware automated quantization with mixed precision"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "pact parameterized clipping activation for quantized neural networks"
    - "wrpn wide reduced precision networks"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "mixed precision training"

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)