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: "attacks meet interpretability attribute steered detection of adversarial samples"
  Abstract: Adversarial sample attacks perturb benign inputs to induce DNN misbehaviors. Recent research has demonstrated the widespread presence and the devastating consequences of such attacks. Existing defense techniques either assume prior knowledge of specific attacks or may not work well on complex models due to their underlying assumptions. We argue that adversarial sample attacks are deeply entangled with interpretability of DNN models: while classification results on benign inputs can be reasoned based on the human perceptible features/attributes, results on adversarial samples can hardly be explained. Therefore, we propose a novel adversarial sample detection technique for face recognition models, based on interpretability. It features a novel bi-directional correspondence inference between attributes and internal neurons to identify neurons critical for individual attributes. The activation values of critical neurons are enhanced to amplify the reasoning part of the computation and the values of other neurons are weakened to suppress the uninterpretable part. The classification results after such transformation are compared with those of the original model to detect adversaries. Results show that our technique can achieve 94% detection accuracy for 7 different kinds of attacks with 9.91% false positives on benign inputs. In contrast, a state-of-the-art feature squeezing technique can only achieve 55% accuracy with 23.3% false positives.

Connected through hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey"):
  with members:
    - "securing future autonomous connected vehicles challenges posed by adversarial machine learning and the way forward"
    - "reproducibility in machine learning for health"
    - "fairness in algorithmic decision making an excursion through the lens of causality"
    - "caveat emptor the risks of using big data for human development"
    - "motion corrected multishot mri reconstruction using generative networks with sensitivity encoding"
    - "the adversarial machine learning conundrum can the insecurity of ml become the achilles heel of cognitive networks"
    - "self supervised domain adaptation for computer vision tasks"
    - "feature robustness in non stationary health records caveats to deployable model performance in common clinical machine learning tasks"

Connected through hyperedge HE-2 (induced by source paper "overfitting in adversarially robust deep learning"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "deep double descent where bigger models and more data hurt"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "me net towards effective adversarial robustness with matrix estimation"

Connected through hyperedge HE-3 (induced by source paper "fast is better than free revisiting adversarial training"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "me net towards effective adversarial robustness with matrix estimation"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "adversarial training for free"
    - "adversarial robustness against the union of multiple perturbation models"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"

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"
    - "stochastic activation pruning for robust adversarial defense"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "massif interactive interpretation of adversarial attacks on deep learning"):
  with members:
    - "a survey of deep learning techniques for autonomous driving"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "adversarial examples in the physical world"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "semanticadv generating adversarial examples via attribute conditional image editing"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "spatially transformed adversarial examples"
    - "cosface large margin cosine loss for deep face recognition"
    - "accelerated training for massive classification via dynamic class selection"
    - "large scale gan training for high fidelity natural image synthesis"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "adversarial robustness vs model compression or both"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "a direct approach to robust deep learning using adversarial networks"
    - "defensive quantization when efficiency meets robustness"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "sparse dnns with improved adversarial robustness"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-8 (induced by source paper "structure invariant testing for machine translation"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "machine learning testing survey landscapes and horizons"
    - "defensive quantization when efficiency meets robustness"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "achieving human parity on automatic chinese to english news translation"
    - "code2vec learning distributed representations of code"
    - "on adversarial examples for character level neural machine translation"

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)