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: "learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization"
  Abstract: Deep Neural Networks (DNNs) are widely applied in a wide range of usecases. There is an increased demand for deploying DNNs on devices that do not have abundant resources such as memory and computation units. Recently, network compression through a variety of techniques such as pruning and quantization have been proposed to reduce the resource requirement. A key parameter that all existing compression techniques are sensitive to is the compression ratio (e.g., pruning sparsity, quantization bitwidth) of each layer. Traditional solutions treat the compression ratios of each layer as hyper-parameters, and tune them using human heuristic. Recent researchers start using black-box hyper-parameter optimizations, but they will introduce new hyper-parameters and have efficiency issue. In this paper, we propose a framework to jointly prune and quantize the DNNs automatically according to a target model size without using any hyper-parameters to manually set the compression ratio for each layer. In the experiments, we show that our framework can compress the weights data of ResNet-50 to be 836x smaller without accuracy loss on CIFAR-10, and compress AlexNet to be 205x smaller without accuracy loss on ImageNet classification.

Connected through hyperedge HE-1 (induced by source paper "adversarially trained model compression when robustness meets efficiency"):
  with members:
    - "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"
    - "to compress or not to compress understanding the interactions between adversarial attacks and neural network compression"

Connected through hyperedge HE-2 (induced by source paper "model compression with adversarial robustness a unified optimization framework"):
  with members:
    - "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"
    - "to compress or not to compress understanding the interactions between adversarial attacks and neural network compression"

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)