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: "a large scale investigation of obfuscation use in google play"
  Abstract: Android applications are frequently plagiarized or maliciously repackaged, and software obfuscation is a popular protection against these practices. In this study, we present the first comprehensive analysis of the use and challenges of software obfuscation in Android applications. We surveyed 308 Google Play developers about their experiences with obfuscation, finding that the free ProGuard software is by far the most commonly used obfuscation tool. With this insight, we analyzed 1.7 million Android apps from Google Play, finding that only 24.9% of apps are obfuscated by the primary developer. This is surprising, given that the most common integrated development environment for Android, Android Studio, includes ProGuard by default. We investigated root causes of this low rate of obfuscation in an in-depth study with 79 Google Play developers, assessing their experiences with obfuscation and asking them to obfuscate a sample app using ProGuard. #R##N#We found that while developers feel that apps in general are at risk of malicious repackaging or plagiarism, they do not fear theft of their own intellectual property. Developers also report difficulties applying obfuscation for their own apps, which was substantiated when they demonstrated problems with all but the most basic configurations to obfuscate our sample app. Our findings indicate that more work is needed to make the application of obfuscation more usable and to educate developers on the risk of their apps being reverse engineered, their intellectual property stolen and their apps being repackaged and redistributed as malware.

Connected through hyperedge HE-1 (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"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "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-2 (induced by source paper "spellbound defending against package typosquatting"):
  with members:
    - "an empirical analysis of the python package index pypi"
    - "small world with high risks a study of security threats in the npm ecosystem"
    - "the landscape of domain name typosquatting techniques and countermeasures"
    - "impact assessment for vulnerabilities in open source software libraries"

Connected through hyperedge HE-3 (induced by source paper "hidden in plain sight obfuscated strings threatening your privacy"):
  with members:
    - "understanding android obfuscation techniques a large scale investigation in the wild"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "an investigation into the use of common libraries in android apps"

Connected through hyperedge HE-4 (induced by source paper "security issues in language based sofware ecosystems"):
  with members:
    - "impact assessment for vulnerabilities in open source software libraries"

Connected through hyperedge HE-5 (induced by source paper "eight years of rider measurement in the android malware ecosystem evolution and lessons learned"):
  with members:
    - "tesseract eliminating experimental bias in malware classification across space and time"
    - "beyond google play a large scale comparative study of chinese android app markets"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "reviewer integration and performance measurement for malware detection"
    - "an investigation into the use of common libraries in android apps"

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)