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: "stochastic activation pruning for robust adversarial defense"
  Abstract: Neural networks are known to be vulnerable to adversarial examples. Carefully chosen perturbations to real images, while imperceptible to humans, induce misclassification and threaten the reliability of deep learning systems in the wild. To guard against adversarial examples, we take inspiration from game theory and cast the problem as a minimax zero-sum game between the adversary and the model. In general, for such games, the optimal strategy for both players requires a stochastic policy, also known as a mixed strategy. In this light, we propose Stochastic Activation Pruning (SAP), a mixed strategy for adversarial defense. SAP prunes a random subset of activations (preferentially pruning those with smaller magnitude) and scales up the survivors to compensate. We can apply SAP to pretrained networks, including adversarially trained models, without fine-tuning, providing robustness against adversarial examples. Experiments demonstrate that SAP confers robustness against attacks, increasing accuracy and preserving calibration.

Connected through hyperedge HE-1 (induced by source paper "post training piecewise linear quantization for deep neural networks"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "low bit quantization of neural networks for efficient inference"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "fighting quantization bias with bias"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "model compression via distillation and quantization"

Connected through hyperedge HE-2 (induced by source paper "on pruning adversarially robust neural networks"):
  with members:
    - "picking winning tickets before training by preserving gradient flow"
    - "certified adversarial robustness via randomized smoothing"
    - "intriguing properties of adversarial training"
    - "robust sparse regularization simultaneously optimizing neural network robustness and compactness"
    - "towards compact and robust deep neural networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "towards stable and efficient training of verifiably robust neural networks"

Connected through hyperedge HE-3 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems"):
  with members:
    - "taxonomy and challenges of out of band signal injection attacks and defenses"
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction"
    - "advhat real world adversarial attack on arcface face id system"
    - "spatially transformed adversarial examples"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "weighted average precision adversarial example detection in the visual perception of autonomous vehicles"):
  with members:
    - "enhancing cross task transferability of adversarial examples with dispersion reduction"
    - "autonomous human activity classification from ego vision camera and accelerometer data"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "scaling provable adversarial defenses"
    - "autonomously and simultaneously refining deep neural network parameters by a bi generative adversarial network aided genetic algorithm"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-5 (induced by source paper "scalable quantitative verification for deep neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "quantitative verification of neural networks and its security applications"
    - "on evaluating adversarial robustness"
    - "a dual approach to scalable verification of deep networks"
    - "concolic testing for deep neural networks"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "evaluating robustness of neural networks with mixed integer programming"

Connected through hyperedge HE-6 (induced by source paper "randomization matters how to defend against strong adversarial attacks"):
  with members:
    - "on the hardness of robust classification"
    - "certified adversarial robustness via randomized smoothing"
    - "lower bounds on adversarial robustness from optimal transport"
    - "robust attacks against multiple classifiers"
    - "adversarial risk via optimal transport and optimal couplings"
    - "on evaluating adversarial robustness"
    - "theoretical evidence for adversarial robustness through randomization"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"

Connected through hyperedge HE-7 (induced by source paper "towards practical lottery ticket hypothesis for adversarial training"):
  with members:
    - "towards compact and robust deep neural networks"
    - "adversarial training for free"
    - "the difficulty of training sparse neural networks"
    - "sparse dnns with improved adversarial robustness"
    - "to compress or not to compress understanding the interactions between adversarial attacks and neural network compression"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-8 (induced by source paper "a model based derivative free approach to black box adversarial examples bobyqa"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "fake news detection on social media using geometric deep learning"
    - "square attack a query efficient black box adversarial attack via random search"
    - "darts deceiving autonomous cars with toxic signs"
    - "low frequency adversarial perturbation"
    - "robust physical world attacks on deep learning models"
    - "practical black box attacks against machine learning"
    - "towards evaluating the robustness of neural networks"

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)