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: "automatic investigation framework for android malware cyber infrastructures"
  Abstract: The popularity of Android system, not only in the handset devices but also in IoT devices, makes it a very attractive destination for malware. Indeed, malware is expanding at a similar rate targeting such devices that rely, in most cases, on Internet to work properly. The state-of-the-art malware mitigation solutions mainly focus on the detection of the actual malicious Android apps using dy- namic and static analyses features to distinguish malicious apps from benign ones. However, there is a small coverage for the In- ternet/network dimension of the Android malicious apps. In this paper, we present ToGather, an automatic investigation framework that takes the Android malware samples, as input, and produces a situation awareness about the malicious cyber infrastructure of these samples families. ToGather leverages the state-of-the-art graph theory techniques to generate an actionable and granular intelligence to mitigate the threat imposed by the malicious Internet activity of the Android malware apps. We experiment ToGather on real malware samples from various Android families, and the obtained results are interesting and very promising

Connected through hyperedge HE-1 (induced by source paper "portable data driven malware detection using language processing and machine learning techniques on behavioral analysis reports"):
  with members:
    - "android malware detection using deep learning on api method sequences"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "automated dynamic analysis of ransomware benefits limitations and use for detection"
    - "dynalog an automated dynamic analysis framework for characterizing android applications"

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)