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: "deceiving end to end deep learning malware detectors using adversarial examples"
  Abstract: In recent years, deep learning has shown performance breakthroughs in many applications, such as image detection, image segmentation, pose estimation, and speech recognition. However, this comes with a major concern: deep networks have been found to be vulnerable to adversarial examples. Adversarial examples are slightly modified inputs that are intentionally designed to cause a misclassification by the model. In the domains of images and speech, the modifications are so small that they are not seen or heard by humans, but nevertheless greatly affect the classification of the model. #R##N#Deep learning models have been successfully applied to malware detection. In this domain, generating adversarial examples is not straightforward, as small modifications to the bytes of the file could lead to significant changes in its functionality and validity. We introduce a novel loss function for generating adversarial examples specifically tailored for discrete input sets, such as executable bytes. We modify malicious binaries so that they would be detected as benign, while preserving their original functionality, by injecting a small sequence of bytes (payload) in the binary file. We applied this approach to an end-to-end convolutional deep learning malware detection model and show a high rate of detection evasion. Moreover, we show that our generated payload is robust enough to be transferable within different locations of the same file and across different files, and that its entropy is low and similar to that of benign data sections.

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 "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"
    - "learning to evade static pe machine learning malware models via reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "copycat practical adversarial attacks on visualization based malware detection"):
  with members:
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "detecting and classifying android malware using static analysis along with creator information"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "microsoft malware classification challenge"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "lightweight classification of iot malware based on image recognition"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-4 (induced by source paper "generation evaluation of adversarial examples for malware obfuscation"):
  with members:
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "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-5 (induced by source paper "random capsnet forest model for imbalanced malware type classification task"):
  with members:
    - "convolutional capsule network for classification of breast cancer histology images"
    - "capsacc an efficient hardware accelerator for capsulenets with data reuse"
    - "fast capsnet for lung cancer screening"
    - "deep transfer learning for static malware classification"
    - "brain tumor type classification via capsule networks"
    - "microsoft malware classification challenge"
    - "deep learning at the shallow end malware classification for non domain experts"
    - "capsule networks against medical imaging data challenges"

Connected through hyperedge HE-6 (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"
    - "learning to evade static pe machine learning malware models via reinforcement learning"
    - "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-7 (induced by source paper "adversarial malware binaries evading deep learning for malware detection in executables"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "hardening classifiers against evasion the good the bad and the ugly"
    - "adversarial detection of flash malware limitations and open issues"
    - "language modeling with gated convolutional networks"
    - "the mythos of model interpretability"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "evasion attacks against machine learning at test time"

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)