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: "extending defensive distillation"
  Abstract: Machine learning is vulnerable to adversarial examples: inputs carefully modified to force misclassification. Designing defenses against such inputs remains largely an open problem. In this work, we revisit defensive distillation---which is one of the mechanisms proposed to mitigate adversarial examples---to address its limitations. We view our results not only as an effective way of addressing some of the recently discovered attacks but also as reinforcing the importance of improved training techniques.

Connected through hyperedge HE-1 (induced by source paper "on the security of randomized defenses against adversarial samples"):
  with members:
    - "breaking certified defenses semantic adversarial examples with spoofed robustness certificates"
    - "certified adversarial robustness via randomized smoothing"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "on evaluating adversarial robustness"
    - "training verified learners with learned verifiers"
    - "breaking transferability of adversarial samples with randomness"
    - "semidefinite relaxations for certifying robustness to adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "testing dnn image classifier for confusion bias errors"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "metric learning for adversarial robustness"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "noise tolerant fair classification"
    - "scaling provable adversarial defenses"
    - "formal security analysis of neural networks using symbolic intervals"
    - "visual interpretability for deep learning a survey"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-3 (induced by source paper "testing and verification of neural network based safety critical control software a systematic literature review"):
  with members:
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "local rule based explanations of black box decision systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "experimental resilience assessment of an open source driving agent"
    - "interpreting deep classifier by visual distillation of dark knowledge"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "software engineering challenges of deep learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "adversarially trained model compression when robustness meets efficiency"):
  with members:
    - "learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "model compression with adversarial robustness a unified optimization framework"):
  with members:
    - "learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "refit a unified watermark removal framework for deep learning systems with limited data"):
  with members:
    - "robust watermarking of neural network with exponential weighting"
    - "strip a defence against trojan attacks on deep neural networks"
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"
    - "effectiveness of distillation attack and countermeasure on neural network watermarking"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "spectral signatures in backdoor attacks"

Connected through hyperedge HE-7 (induced by source paper "enhancing gradient based attacks with symbolic intervals"):
  with members:
    - "scaling provable adversarial defenses"
    - "formal security analysis of neural networks using symbolic intervals"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "mixtrain scalable training of formally robust neural networks"
    - "efficient formal safety analysis of neural networks"

Connected through hyperedge HE-8 (induced by source paper "defense against adversarial images using web scale nearest neighbor search"):
  with members:
    - "retrieval augmented convolutional neural networks for improved robustness against adversarial examples"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "exploring the limits of weakly supervised pretraining"
    - "black box adversarial attacks with limited queries and information"
    - "audio adversarial examples targeted attacks on speech to text"

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)