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: "on security and sparsity of linear classifiers for adversarial settings"
  Abstract: Machine-learning techniques are widely used in security-related applications, like spam and malware detection. However, in such settings, they have been shown to be vulnerable to adversarial attacks, including the deliberate manipulation of data at test time to evade detection. In this work, we focus on the vulnerability of linear classifiers to evasion attacks. This can be considered a relevant problem, as linear classifiers have been increasingly used in embedded systems and mobile devices for their low processing time and memory requirements. We exploit recent findings in robust optimization to investigate the link between regularization and security of linear classifiers, depending on the type of attack. We also analyze the relationship between the sparsity of feature weights, which is desirable for reducing processing cost, and the security of linear classifiers. We further propose a novel octagonal regularizer that allows us to achieve a proper trade-off between them. Finally, we empirically show how this regularizer can improve classifier security and sparsity in real-world application examples including spam and malware detection.

Connected through hyperedge HE-1 (induced by source paper "adversarial training and robustness for multiple perturbations"):
  with members:
    - "transfer of adversarial robustness between perturbation types"
    - "adversarial perceptual ad blocking meets adversarial machine learning"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial training for free"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "on the geometry of adversarial examples"
    - "towards the first adversarially robust neural network model on mnist"

Connected through hyperedge HE-2 (induced by source paper "hardening random forest cyber detectors against adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "sok security and privacy in machine learning"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "towards evaluating the robustness of neural networks"
    - "deepdga adversarially tuned domain generation and detection"

Connected through hyperedge HE-3 (induced by source paper "wild patterns ten years after the rise of adversarial machine learning"):
  with members:
    - "poisoning behavioral malware clustering"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "is feature selection secure against training data poisoning"
    - "is data clustering in adversarial settings secure"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "certified defenses for data poisoning attacks"
    - "deltaphish detecting phishing webpages in compromised websites"

Connected through hyperedge HE-4 (induced by source paper "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "is feature selection secure against training data poisoning"
    - "black box adversarial attacks with limited queries and information"
    - "stealing hyperparameters in machine learning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "yes machine learning can be more secure a case study on android malware detection"

Connected through hyperedge HE-5 (induced by source paper "learning universal adversarial perturbations with generative models"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial attacks on neural network policies"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "adversarial and clean data are not twins"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "simple black box adversarial perturbations for deep networks"
    - "on the statistical detection of adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "is deep learning safe for robot vision adversarial examples against the icub humanoid"):
  with members:
    - "a boundary tilting persepective on the phenomenon of adversarial examples"
    - "adversarial examples detection in deep networks with convolutional filter statistics"
    - "adversarial examples in the physical world"
    - "improving the robustness of deep neural networks via stability training"
    - "foveation based mechanisms alleviate adversarial examples"
    - "towards open set deep networks"
    - "the limitations of deep learning in adversarial settings"
    - "deepfool a simple and accurate method to fool deep 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)