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: "attack and defense of dynamic analysis based adversarial neural malware classification models"
  Abstract: Recently researchers have proposed using deep learning-based systems for malware detection. Unfortunately, all deep learning classification systems are vulnerable to adversarial attacks. Previous work has studied adversarial attacks against static analysis-based malware classifiers which only classify the content of the unknown file without execution. However, since the majority of malware is either packed or encrypted, malware classification based on static analysis often fails to detect these types of files. To overcome this limitation, anti-malware companies typically perform dynamic analysis by emulating each file in the anti-malware engine or performing in-depth scanning in a virtual machine. These strategies allow the analysis of the malware after unpacking or decryption. In this work, we study different strategies of crafting adversarial samples for dynamic analysis. These strategies operate on sparse, binary inputs in contrast to continuous inputs such as pixels in images. We then study the effects of two, previously proposed defensive mechanisms against crafted adversarial samples including the distillation and ensemble defenses. We also propose and evaluate the weight decay defense. Experiments show that with these three defensive strategies, the number of successfully crafted adversarial samples is reduced compared to a standard baseline system without any defenses. In particular, the ensemble defense is the most resilient to adversarial attacks. Importantly, none of the defenses significantly reduce the classification accuracy for detecting malware. Finally, we demonstrate that while adding additional hidden layers to neural models does not significantly improve the malware classification accuracy, it does significantly increase the classifier's robustness to adversarial attacks.

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 "defense methods against adversarial examples for recurrent neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generating natural language adversarial examples"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "robust neural malware detection models for emulation sequence learning"

Connected through hyperedge HE-3 (induced by source paper "effectiveness of adversarial examples and defenses for malware classification"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "black box attacks against rnn based malware detection algorithms"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "hardening malware detection systems against cyber maneuvers an adversarial machine learning approach"
    - "improved training of wasserstein gans"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "generic black box end to end attack against state of the art api call based malware classifiers"

Connected through hyperedge HE-4 (induced by source paper "integration of static and dynamic analysis for malware family classification with composite neural network"):
  with members:
    - "theoretical investigation of composite neural network"
    - "behavioral malware classification using convolutional recurrent neural networks"
    - "a family of droids android malware detection via behavioral modeling static vs dynamic analysis"
    - "robust neural malware detection models for emulation sequence learning"
    - "virtual machine introspection based malware behavior profiling and family grouping"
    - "attention is all you need"
    - "dynamic analysis of executables to detect and characterize malware"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-5 (induced by source paper "malytics a malware detection scheme"):
  with members:
    - "yes machine learning can be more secure a case study on android malware detection"
    - "dynamic routing between capsules"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "fast memory efficient anomaly detection in streaming heterogeneous graphs"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"
    - "towards evaluating the robustness of 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.CR(Cryptography and Security)