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 examples opportunities and challenges"
  Abstract: Deep neural networks (DNNs) have shown huge superiority over humans in image recognition, speech processing, autonomous vehicles and medical diagnosis. However, recent studies indicate that DNNs are vulnerable to adversarial examples (AEs), which are designed by attackers to fool deep learning models. Different from real examples, AEs can mislead the model to predict incorrect outputs while hardly be distinguished by human eyes, therefore threaten security-critical deep-learning applications. In recent years, the generation and defense of AEs have become a research hotspot in the field of artificial intelligence (AI) security. This article reviews the latest research progress of AEs. First, we introduce the concept, cause, characteristics and evaluation metrics of AEs, then give a survey on the state-of-the-art AE generation methods with the discussion of advantages and disadvantages. After that, we review the existing defenses and discuss their limitations. Finally, future research opportunities and challenges on AEs are prospected.

Connected through hyperedge HE-1 (induced by source paper "ai safety state of the field through quantitative lens"):
  with members:
    - "fairness in learning based sequential decision algorithms a survey"
    - "dave i can assure you that it s going to be all right a definition case for and survey of algorithmic assurances in human autonomy trust relationships"
    - "explainability in human agent systems"
    - "classification schemas for artificial intelligence failures"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "a survey on bias and fairness in machine learning"
    - "machine learning testing survey landscapes and horizons"
    - "unexplainability and incomprehensibility of artificial intelligence"

Connected through hyperedge HE-2 (induced by source paper "adversarial robustness via label smoothing"):
  with members:
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "darts deceiving autonomous cars with toxic signs"
    - "regularizing neural networks by penalizing confident output distributions"
    - "a boundary tilting persepective on the phenomenon of adversarial examples"
    - "on the effectiveness of defensive distillation"
    - "towards evaluating the robustness of neural networks"
    - "robustness of classifiers from adversarial to random noise"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-3 (induced by source paper "there is limited correlation between coverage and robustness for deep neural networks"):
  with members:
    - "efficient and accurate estimation of lipschitz constants for deep neural networks"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "guiding deep learning system testing using surprise adequacy"
    - "testing deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "adversarial robustness via adversarial label smoothing"):
  with members:
    - "label smoothing and logit squeezing a replacement for adversarial training"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "darts deceiving autonomous cars with toxic signs"
    - "regularizing neural networks by penalizing confident output distributions"
    - "a boundary tilting persepective on the phenomenon of adversarial examples"
    - "on the effectiveness of defensive distillation"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-5 (induced by source paper "snn under attack are spiking deep belief networks vulnerable to adversarial examples"):
  with members:
    - "deep learning in spiking neural networks"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "are adversarial examples inevitable"
    - "fademl understanding the impact of pre processing noise filtering on adversarial machine learning"
    - "weighted contrastive divergence"
    - "enhancing robustness of machine learning systems via data transformations"
    - "evt_mnist a spike based version of traditional mnist"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-6 (induced by source paper "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"):
  with members:
    - "detecting and classifying android malware using static analysis along with creator information"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "microsoft malware classification challenge"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"
    - "r2 d2 color inspired convolutional neural network cnn based android malware detections"
    - "towards evaluating the robustness of neural networks"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-7 (induced by source paper "understanding adversarial robustness through loss landscape geometries"):
  with members:
    - "adversarial training can hurt generalization"
    - "spatially transformed adversarial examples"
    - "implicit regularization in deep learning"
    - "visualizing the loss landscape of neural nets"
    - "understanding deep learning requires rethinking generalization"
    - "adversarial machine learning at scale"
    - "deep residual learning for image recognition"

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)