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: "explaining vulnerabilities of deep learning to adversarial malware binaries"
  Abstract: Recent work has shown that deep-learning algorithms for malware detection are also susceptible to adversarial examples, i.e., carefully-crafted perturbations to input malware that enable misleading classification. Although this has questioned their suitability for this task, it is not yet clear why such algorithms are easily fooled also in this particular application domain. In this work, we take a first step to tackle this issue by leveraging explainable machine-learning algorithms developed to interpret the black-box decisions of deep neural networks. In particular, we use an explainable technique known as feature attribution to identify the most influential input features contributing to each decision, and adapt it to provide meaningful explanations to the classification of malware binaries. In this case, we find that a recently-proposed convolutional neural network does not learn any meaningful characteristic for malware detection from the data and text sections of executable files, but rather tends to learn to discriminate between benign and malware samples based on the characteristics found in the file header. Based on this finding, we propose a novel attack algorithm that generates adversarial malware binaries by only changing few tens of bytes in the file header. With respect to the other state-of-the-art attack algorithms, our attack does not require injecting any padding bytes at the end of the file, and it is much more efficient, as it requires manipulating much fewer bytes.

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"
    - "easy to fool testing the anti evasion capabilities of pdf malware scanners"

Connected through hyperedge HE-2 (induced by source paper "waf a mole evading web application firewalls through adversarial machine learning"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "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 "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"
    - "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"
    - "learning to evade static pe machine learning malware models via reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "there are no bit parts for sign bits in black box attacks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "exploring the space of black box attacks on deep neural networks"
    - "signsgd compressed optimisation for non convex problems"

Connected through hyperedge HE-5 (induced by source paper "malware evasion attack and defense"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "ember an open dataset for training static pe malware machine learning models"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "enhancing robustness of machine learning systems via data transformations"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "generation evaluation of adversarial examples for malware obfuscation"):
  with members:
    - "deceiving end to end deep learning malware detectors using adversarial examples"
    - "microsoft malware classification challenge"
    - "black box attacks against rnn based malware detection algorithms"
    - "imbalanced malware images classification a cnn based approach"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "xgboost a scalable tree boosting system"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-7 (induced by source paper "would a file by any other name seem as malicious"):
  with members:
    - "activation analysis of a byte based deep neural network for malware classification"
    - "learning from context exploiting and interpreting file path information for better malware detection"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "ember an open dataset for training static pe malware machine learning models"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "text understanding from scratch"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "towards adversarial malware detection lessons learned from pdf based attacks"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "model reuse attacks on deep learning systems"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "explaining black box android malware detection"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "digital investigation of pdf files unveiling traces of embedded malware"

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)