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 weight pruning of deep neural networks using admm"
  Abstract: Deep neural networks (DNNs) although achieving human-level performance in many domains, have very large model size that hinders their broader applications on edge computing devices. Extensive research work have been conducted on DNN model compression or pruning. However, most of the previous work took heuristic approaches. This work proposes a progressive weight pruning approach based on ADMM (Alternating Direction Method of Multipliers), a powerful technique to deal with non-convex optimization problems with potentially combinatorial constraints. Motivated by dynamic programming, the proposed method reaches extremely high pruning rate by using partial prunings with moderate pruning rates. Therefore, it resolves the accuracy degradation and long convergence time problems when pursuing extremely high pruning ratios. It achieves up to 34 times pruning rate for ImageNet dataset and 167 times pruning rate for MNIST dataset, significantly higher than those reached by the literature work. Under the same number of epochs, the proposed method also achieves faster convergence and higher compression rates. The codes and pruned DNN models are released in the link bit.ly/2zxdlss

Connected through hyperedge HE-1 (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"
    - "spiking deep residual network"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

Connected through hyperedge HE-2 (induced by source paper "autoslim an automatic dnn structured pruning framework for ultra high compression rates"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "rethinking the value of network pruning"
    - "nisp pruning networks using neuron importance score propagation"
    - "large scale evolution of image classifiers"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-3 (induced by source paper "cae p compressive autoencoder with pruning based on admm"):
  with members:
    - "conditional probability models for deep image compression"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "an end to end compression framework based on convolutional neural networks"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "learning convolutional networks for content weighted image compression"
    - "channel pruning for accelerating very deep neural networks"
    - "full resolution image compression with recurrent neural networks"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "single shot channel pruning based on alternating direction method of multipliers"):
  with members:
    - "fast converging conditional generative adversarial networks for image synthesis"
    - "efficient sparse winograd convolutional neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "large scale gan training for high fidelity natural image synthesis"
    - "snip single shot network pruning based on connection sensitivity"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "thinet a filter level pruning method for deep neural network compression"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "distributed power control for large energy harvesting networks a multi agent deep reinforcement learning approach"):
  with members:
    - "accelerated structure aware reinforcement learning for delay sensitive energy harvesting wireless sensors"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "multi agent reinforcement learning for energy harvesting two hop communications with full cooperation"
    - "distributed power control for delay optimization in energy harvesting cooperative relay networks"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "dynamic network surgery for efficient dnns"
    - "multi access communications with energy harvesting a multi armed bandit model and the optimality of the myopic policy"
    - "iterative dynamic water filling for fading multiple access channels with energy harvesting"

Connected through hyperedge HE-6 (induced by source paper "cae admm implicit bitrate optimization via admm based pruning in compressive autoencoders"):
  with members:
    - "an end to end compression framework based on convolutional neural networks"
    - "fast and accurate image super resolution with deep laplacian pyramid networks"
    - "learning convolutional networks for content weighted image compression"
    - "channel pruning for accelerating very deep neural networks"
    - "full resolution image compression with recurrent neural networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "variable rate image compression with recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "a unified framework of dnn weight pruning and weight clustering quantization using admm"):
  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"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "theoretical properties for neural networks with weight matrices of low displacement rank"
    - "nest a neural network synthesis tool based on a grow and prune paradigm"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "net trim convex pruning of deep neural networks with performance guarantee"

Connected through hyperedge HE-8 (induced by source paper "differentiable fine grained quantization for deep neural network compression"):
  with members:
    - "proxquant quantized neural networks via proximal operators"
    - "darts differentiable architecture search"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "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.LG(Machine Learning)