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 attacks and defences a survey"
  Abstract: Deep learning has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past. In the last few years, deep learning has advanced radically in such a way that it can surpass human-level performance on a number of tasks. As a consequence, deep learning is being extensively used in most of the recent day-to-day applications. However, security of deep learning systems are vulnerable to crafted adversarial examples, which may be imperceptible to the human eye, but can lead the model to misclassify the output. In recent times, different types of adversaries based on their threat model leverage these vulnerabilities to compromise a deep learning system where adversaries have high incentives. Hence, it is extremely important to provide robustness to deep learning algorithms against these adversaries. However, there are only a few strong countermeasures which can be used in all types of attack scenarios to design a robust deep learning system. In this paper, we attempt to provide a detailed discussion on different types of adversarial attacks with various threat models and also elaborate the efficiency and challenges of recent countermeasures against them.

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 "understanding the intrinsic robustness of image distributions using conditional generative models"):
  with members:
    - "on adaptive attacks to adversarial example defenses"
    - "lower bounds on adversarial robustness from optimal transport"
    - "empirically measuring concentration fundamental limits on intrinsic robustness"
    - "theoretically principled trade off between robustness and accuracy"
    - "scaling provable adversarial defenses"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "mixtrain scalable training of formally robust neural networks"
    - "adversarial spheres"

Connected through hyperedge HE-3 (induced by source paper "conaml constrained adversarial machine learning for cyber physical systems"):
  with members:
    - "textbugger generating adversarial text against real world applications"
    - "dynamic detection of false data injection attack in smart grid using deep learning"
    - "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system"
    - "adversarial regression for detecting attacks in cyber physical systems"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "a deep learning based framework for conducting stealthy attacks in industrial control systems"
    - "anomaly detection for a water treatment system using unsupervised machine learning"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-4 (induced by source paper "fine grained uncertainty modeling in neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual interpretability for deep learning a survey"
    - "uncertainty aware ai ml why and how"
    - "evidential deep learning to quantify classification uncertainty"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-5 (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable"):
  with members:
    - "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis"
    - "learning the invisible a hybrid deep learning shearlet framework for limited angle computed tomography"
    - "image reconstruction by domain transform manifold learning"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-6 (induced by source paper "adversarial transferability in wearable sensor systems"):
  with members:
    - "cross domain transferability of adversarial perturbations"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "towards evaluating the robustness of neural networks"
    - "the limitations of deep learning in adversarial settings"
    - "evasion attacks against machine learning at test time"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-7 (induced by source paper "dla dense layer analysis for adversarial example detection"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "audio adversarial examples targeted attacks on speech to text"
    - "testing deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-8 (induced by source paper "adversarial security attacks and perturbations on machine learning and deep learning methods"):
  with members:
    - "on evaluating adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "designing adversarially resilient classifiers using resilient feature engineering"
    - "security for machine learning based systems attacks and challenges during training and inference"
    - "security matters a survey on adversarial machine learning"
    - "security and privacy issues in deep learning"
    - "deep text classification can be fooled"
    - "mlcapsule guarded offline deployment of machine learning as a service"

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)