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 malware binaries evading deep learning for malware detection in executables"
  Abstract: Machine-learning methods have already been exploited as useful tools for detecting malicious executable files. They leverage data retrieved from malware samples, such as header fields, instruction sequences, or even raw bytes, to learn models that discriminate between benign and malicious software. However, it has also been shown that machine learning and deep neural networks can be fooled by evasion attacks (also referred to as adversarial examples), i.e., small changes to the input data that cause misclassification at test time. In this work, we investigate the vulnerability of malware detection methods that use deep networks to learn from raw bytes. We propose a gradient-based attack that is capable of evading a recently-proposed deep network suited to this purpose by only changing few specific bytes at the end of each malware sample, while preserving its intrusive functionality. Promising results show that our adversarial malware binaries evade the targeted network with high probability, even though less than 1% of their bytes are modified.

Connected through hyperedge HE-1 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers"):
  with members:
    - "understanding the efficacy reliability and resiliency of computer vision techniques for malware detection and future research directions"
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "on defending against label flipping attacks on malware detection systems"
    - "adversarial samples on android malware detection systems for iot systems"
    - "copycat practical adversarial attacks on visualization based malware detection"
    - "generation evaluation of adversarial examples for malware obfuscation"
    - "examining adversarial learning against graph based iot malware detection systems"
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"

Connected through hyperedge HE-2 (induced by source paper "waf a mole evading web application firewalls through adversarial machine learning"):
  with members:
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "black box adversarial attacks with limited queries and information"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "generic black box end to end attack against state of the art api call based malware classifiers"
    - "on the statistical detection of adversarial examples"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-3 (induced by source paper "feature level malware obfuscation in deep learning"):
  with members:
    - "adversarial attacks and defenses in images graphs and text a review"
    - "effectiveness of adversarial examples and defenses for malware classification"
    - "a large scale investigation of obfuscation use in google play"
    - "exploring adversarial examples in malware detection"
    - "deep learning at the shallow end malware classification for non domain experts"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "motivating the rules of the game for adversarial example research"
    - "adversarial perturbations against deep neural networks for malware classification"

Connected through hyperedge HE-4 (induced by source paper "mdea malware detection with evolutionary adversarial learning"):
  with members:
    - "the advantages of multiple classes for reducing overfitting from test set reuse"
    - "learning to evade static pe machine learning malware models via reinforcement learning"
    - "regularized evolution for image classifier architecture search"
    - "evolutionary generative adversarial networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-5 (induced by source paper "optimization guided binary diversification to mislead neural networks for malware detection"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "misleading authorship attribution of source code using adversarial learning"
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "theoretically principled trade off between robustness and accuracy"
    - "counterstrike defending deep learning architectures against adversarial samples by langevin dynamics with supervised denoising autoencoder"
    - "ember an open dataset for training static pe malware machine learning models"
    - "feature denoising for improving adversarial robustness"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "adversarial examples for deep learning cyber security analytics"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "certified adversarial robustness via randomized smoothing"
    - "on designing machine learning models for malicious network traffic classification"
    - "rallying adversarial techniques against deep learning for network security"
    - "evading classifiers in discrete domains with provable optimality guarantees"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deceiving end to end deep learning malware detectors using adversarial examples"

Connected through hyperedge HE-7 (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"

Connected through hyperedge HE-8 (induced by source paper "the threat of adversarial attacks on machine learning in network security a survey"):
  with members:
    - "improving network intrusion detection classiﬁers by non payload based exploit independent obfuscations an adversarial approach"
    - "detecting cyberattacks in industrial control systems using convolutional neural networks"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "an adaptive system for detecting malicious queries in web attacks"
    - "exploring adversarial examples in malware detection"
    - "is feature selection secure against training data poisoning"
    - "adversarially robust generalization requires more data"
    - "idsgan generative adversarial networks for attack generation against intrusion detection"

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.CR(Cryptography and Security)