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: "learning to evade static pe machine learning malware models via reinforcement learning"
  Abstract: Machine learning is a popular approach to signatureless malware detection because it can generalize to never-before-seen malware families and polymorphic strains. This has resulted in its practical use for either primary detection engines or for supplementary heuristic detection by anti-malware vendors. Recent work in adversarial machine learning has shown that deep learning models are susceptible to gradient-based attacks, whereas non-differentiable models that report a score can be attacked by genetic algorithms that aim to systematically reduce the score. We propose a more general framework based on reinforcement learning (RL) for attacking static portable executable (PE) anti-malware engines. The general framework does not require a differentiable model nor does it require the engine to produce a score. Instead, an RL agent is equipped with a set of functionality-preserving operations that it may perform on the PE file. Through a series of games played against the anti-malware engine, it learns which sequences of operations are likely to result in evading the detector for any given malware sample. This enables completely black-box attacks against static PE anti-malware, and produces functional evasive malware samples as a direct result. We show in experiments that our method can attack a gradient-boosted machine learning model with evasion rates that are substantial and appear to be strongly dependent on the dataset. We demonstrate that attacks against this model appear to also evade components of publicly hosted antivirus engines. Adversarial training results are also presented: by retraining the model on evasive ransomware samples, a subsequent attack is 33% less effective. However, there are overfitting dangers when adversarial training, which we note. We release code to allow researchers to reproduce and improve this approach.

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 "mdea malware detection with evolutionary adversarial learning"):
  with members:
    - "the advantages of multiple classes for reducing overfitting from test set reuse"
    - "regularized evolution for image classifier architecture search"
    - "evolutionary generative adversarial networks"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "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-3 (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-4 (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-5 (induced by source paper "deep detector health management under adversarial campaigns"):
  with members:
    - "gradient similarity an explainable approach to detect adversarial attacks against deep learning"
    - "enabling trust in deep learning models a digital forensics case study"
    - "exploring adversarial examples in malware detection"
    - "motivating the rules of the game for adversarial example research"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "towards evaluating the robustness of neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-6 (induced by source paper "identify susceptible locations in medical records via adversarial attacks on deep predictive models"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "scalable and accurate deep learning for electronic health records"
    - "analysis of universal adversarial perturbations"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "adversarial examples for evaluating reading comprehension systems"
    - "generating natural adversarial examples"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "adversarial diversity and hard positive generation"

Connected through hyperedge HE-7 (induced by source paper "exploring adversarial examples in malware detection"):
  with members:
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "ember an open dataset for training static pe malware machine learning models"
    - "deceiving end to end deep learning malware detectors using adversarial examples"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "black box attacks against rnn based malware detection algorithms"
    - "practical black box attacks against machine learning"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-8 (induced by source paper "query efficient black box attack against sequence based malware classifiers"):
  with members:
    - "generating natural language adversarial examples"
    - "black box adversarial attacks with limited queries and information"
    - "robust neural malware detection models for emulation sequence learning"
    - "query limited black box attacks to classifiers"
    - "black box attacks against rnn based malware detection algorithms"
    - "maximum likelihood augmented discrete generative adversarial networks"
    - "generic black box end to end attack against state of the art api call based malware classifiers"
    - "evading classifiers by morphing in the dark"

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)