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: "systematic weight pruning of dnns using alternating direction method of multipliers"
  Abstract: We present a systematic weight pruning framework of deep neural networks (DNNs) using the alternating direction method of multipliers (ADMM). We first formulate the weight pruning problem of DNNs as a constrained nonconvex optimization problem, and then adopt the ADMM framework for systematic weight pruning. We show that ADMM is highly suitable for weight pruning due to the computational efficiency it offers. We achieve a much higher compression ratio compared with prior work while maintaining the same test accuracy, together with a faster convergence rate.

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"
    - "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm"
    - "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"

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"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "rethinking the value of network pruning"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-3 (induced by source paper "rtmobile beyond real time mobile acceleration of rnns for speech recognition"):
  with members:
    - "deepsz a novel framework to compress deep neural networks by using error bounded lossy compression"
    - "protecting neural networks with hierarchical random switching towards better robustness accuracy trade off for stochastic defenses"
    - "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"
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "e rnn design optimization for efficient recurrent neural networks in fpgas"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "channel wise pruning of neural networks with tapering resource constraint"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "progressive deep neural networks acceleration via soft filter pruning"
    - "the incredible shrinking neural network new perspectives on learning representations through the lens of pruning"
    - "structured probabilistic pruning for convolutional neural network acceleration"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "towards thinner convolutional neural networks through gradually global pruning"

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)