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: "analyzing not a virus bundled adware the wajam case"
  Abstract: Case studies on malicious code mostly focus on botnets and worms (recently revived with IoT devices), prominent pieces of malware or Advanced Persistent Threats, exploit kits, ransomware, yet very little has been done on adware. Previous studies on "unwanted" applications, including adware, favored breadth of analysis, uncovering ties between different actors and distribution methods. We investigate the evolution over nearly six years of a particularly successful and active adware business: Wajam. As of 2016, revealed by the Office of the Privacy Commissioner of Canada, Wajam had "hundreds of millions of installations" and collected 400TB of private information from users. We gather 52 samples of Wajam, released between 2013 to 2018, and analyze the technical evolution from a simple browser add-on to full-fledged obfuscated malware including rootkit, browser process injection, and antivirus evasion capabilities. We uncover its strategy to ensure a low detection rate, which heavily relies on numerous layers of encryption, and more recently on steganography. Furthermore, Wajam leaks the browsing histories of four major browsers, along with the keywords searched by users on highly popular websites. It is also vulnerable to arbitrary content injection on HTTPS webpages, and likely to remote code execution. We show evidence that Wajam is a widespread threat, actively maintained with daily obfuscated samples that are poorly detected by antivirus engines. More worrisome, we found the same evasion techniques in another piece of adware, suggesting that they could be provided by a third-party, and reused in other cases. Finally, we conclude that the adware problem has been overlooked for too long, which can reach (or even surplus) the complexity of advanced malware, and pose both privacy and security risks to users, more so than many well-known and thoroughly-analyzed malware families.

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)