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: "adversarial robustness vs model compression or both"
  Abstract: It is well known that deep neural networks (DNNs) are vulnerable to adversarial attacks, which are implemented by adding crafted perturbations onto benign examples. Min-max robust optimization based adversarial training can provide a notion of security against adversarial attacks. However, adversarial robustness requires a significantly larger capacity of the network than that for the natural training with only benign examples. This paper proposes a framework of concurrent adversarial training and weight pruning that enables model compression while still preserving the adversarial robustness and essentially tackles the dilemma of adversarial training. Furthermore, this work studies two hypotheses about weight pruning in the conventional setting and finds that weight pruning is essential for reducing the network model size in the adversarial setting, training a small model from scratch even with inherited initialization from the large model cannot achieve both adversarial robustness and high standard accuracy. Code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "non structured dnn weight pruning is it beneficial in any platform"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "hypar towards hybrid parallelism for deep learning accelerator array"
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "morph flexible acceleration for 3d cnn based video understanding"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"

Connected through hyperedge HE-2 (induced by source paper "sparsity meets robustness channel pruning for the feynman kac formalism principled robust deep neural nets"):
  with members:
    - "robust sparse regularization simultaneously optimizing neural network robustness and compactness"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarially trained model compression when robustness meets efficiency"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "sparse dnns with improved adversarial robustness"
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "model compression via distillation and quantization"
    - "deep neural nets with interpolating function as output activation"

Connected through hyperedge HE-3 (induced by source paper "robust sparse regularization simultaneously optimizing neural network robustness and compactness"):
  with members:
    - "defensive quantization when efficiency meets robustness"
    - "sparse dnns with improved adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "structured pruning for efficient convnets via incremental regularization"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "certified defenses against adversarial examples"
    - "defend deep neural networks against adversarial examples via fixed anddynamic quantized activation functions"

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.CV(Computer Vision and Pattern Recognition)