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: "droidanalytics a signature based analytic system to collect extract analyze and associate android malware"
  Abstract: Smartphones and mobile devices are rapidly becoming indispensable devices for many users. Unfortunately, they also become fertile grounds for hackers to deploy malware and to spread virus. There is an urgent need to have a "security analytic & forensic system" which can facilitate analysts to examine, dissect, associate and correlate large number of mobile applications. An effective analytic system needs to address the following questions: How to automatically collect and manage a high volume of mobile malware? How to analyze a zero-day suspicious application, and compare or associate it with existing malware families in the database? How to perform information retrieval so to reveal similar malicious logic with existing malware, and to quickly identify the new malicious code segment? In this paper, we present the design and implementation of DroidAnalytics, a signature based analytic system to automatically collect, manage, analyze and extract android malware. The system facilitates analysts to retrieve, associate and reveal malicious logics at the "opcode level". We demonstrate the efficacy of DroidAnalytics using 150,368 Android applications, and successfully determine 2,494 Android malware from 102 different families, with 342 of them being zero-day malware samples from six different families. To the best of our knowledge, this is the first reported case in showing such a large Android malware analysis/detection. The evaluation shows the DroidAnalytics is a valuable tool and is effective in analyzing malware repackaging and mutations.

Connected through hyperedge HE-1 (induced by source paper "a survey on the detection of android malicious apps"):
  with members:
    - "group wise classification approach to improve android malicious apps detection accuracy"
    - "an investigation of the classifiers to detect android malicious apps"
    - "an effective approach for classification of advanced malware with high accuracy"
    - "adaptive and scalable android malware detection through online learning"
    - "improving the detection accuracy of unknown malware by partitioning the executables in groups"
    - "n opcode analysis for android malware classification and categorization"
    - "evolution and detection of polymorphic and metamorphic malwares a survey"

Connected through hyperedge HE-2 (induced by source paper "rebooting research on detecting repackaged android apps literature review and benchmark"):
  with members:
    - "androzoo collecting millions of android apps and their metadata for the research community"
    - "an investigation into the use of common libraries in android apps"
    - "a critical review of automatic patch generation learned from human written patches essay on the problem statement and the evaluation of automatic software repair"

Connected through hyperedge HE-3 (induced by source paper "datasets of android applications a literature review"):
  with members:
    - "androvault constructing knowledge graph from millions of android apps for automated analysis"

Connected through hyperedge HE-4 (induced by source paper "android malware characterization using metadata and machine learning techniques"):
  with members:
    - "high accuracy android malware detection using ensemble learning"
    - "analysis of bayesian classification based approaches for android malware detection"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-5 (induced by source paper "conceptual evidence collection and analysis methodology for android devices"):
  with members:
    - "digital forensic investigation of cloud storage services"
    - "mobile cloud forensics an analysis of seven popular 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)