human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "eight years of rider measurement in the android malware ecosystem evolution and lessons learned"):
  Abstract: Despite the growing threat posed by Android malware, the research community is still lacking a comprehensive view of common behaviors and trends exposed by malware families active on the platform. Without such view, the researchers incur the risk of developing systems that only detect outdated threats, missing the most recent ones. In this paper, we conduct the largest measurement of Android malware behavior to date, analyzing over 1.2 million malware samples that belong to 1.2K families over a period of eight years (from 2010 to 2017). We aim at understanding how the behavior of Android malware has evolved over time, focusing on repackaging malware. In this type of threats different innocuous apps are piggybacked with a malicious payload (rider), allowing inexpensive malware manufacturing. #R##N#One of the main challenges posed when studying repackaged malware is slicing the app to split benign components apart from the malicious ones. To address this problem, we use differential analysis to isolate software components that are irrelevant to the campaign and study the behavior of malicious riders alone. Our analysis framework relies on collective repositories and recent advances on the systematization of intelligence extracted from multiple anti-virus vendors. We find that since its infancy in 2010, the Android malware ecosystem has changed significantly, both in the type of malicious activity performed by the malicious samples and in the level of obfuscation used by malware to avoid detection. We then show that our framework can aid analysts who attempt to study unknown malware families. Finally, we discuss what our findings mean for Android malware detection research, highlighting areas that need further attention by the research community.

Includes members:
  Member "tesseract eliminating experimental bias in malware classification across space and time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on model evaluation under non constant class imbalance")
  Member "beyond google play a large scale comparative study of chinese android app markets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "characterizing cryptocurrency exchange scams")
  Member "a large scale investigation of obfuscation use in google play":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "feature level malware obfuscation in deep learning")
  Member "mamadroid detecting android malware by building markov chains of behavioral models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hidden in plain sight obfuscated strings threatening your privacy")
  Member "reviewer integration and performance measurement for malware detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intriguing properties of adversarial ml attacks in the problem space")
  Member "an investigation into the use of common libraries in android apps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interactive effort aware library version harmonization")

Question: Please tell me which class the source paper 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)