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: "high accuracy android malware detection using ensemble learning"
  Abstract: With over 50 billion downloads and more than 1.3 million apps in Google's official market, Android has continued to gain popularity among smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature-based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus, this study proposes an approach that utilises ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3-99% detection accuracy with very low false positive rates.

Connected through hyperedge HE-1 (induced by source paper "dl droid deep learning based android malware detection using real devices"):
  with members:
    - "improving dynamic analysis of android apps using hybrid test input generation"
    - "android malware detection using deep learning on api method sequences"
    - "emulator vs real phone android malware detection using machine learning"
    - "n gram opcode analysis for android malware detection"
    - "n opcode analysis for android malware classification and categorization"
    - "dynalog an automated dynamic analysis framework for characterizing android applications"
    - "android malware detection an eigenspace analysis approach"
    - "automated test input generation for android are we there yet"

Connected through hyperedge HE-2 (induced by source paper "a survey of machine and deep learning methods for internet of things iot security"):
  with members:
    - "the effect of iot new features on security and privacy new threats existing solutions and challenges yet to be solved"
    - "machine learning ddos detection for consumer internet of things devices"
    - "lifelong learning for sentiment classification"
    - "iot security techniques based on machine learning"
    - "deep learning in mobile and wireless networking a survey"
    - "deep learning for iot big data and streaming analytics a survey"
    - "detection of unauthorized iot devices using machine learning techniques"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-3 (induced by source paper "improving dynamic analysis of android apps using hybrid test input generation"):
  with members:
    - "emulator vs real phone android malware detection using machine learning"
    - "dynalog an automated dynamic analysis framework for characterizing android applications"
    - "android malware detection an eigenspace analysis approach"
    - "automated test input generation for android are we there yet"

Connected through hyperedge HE-4 (induced by source paper "android malware characterization using metadata and machine learning techniques"):
  with members:
    - "analysis of bayesian classification based approaches for android malware detection"
    - "droidanalytics a signature based analytic system to collect extract analyze and associate android malware"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-5 (induced by source paper "emulator vs real phone android malware detection using machine learning"):
  with members:
    - "dynalog an automated dynamic analysis framework for characterizing android applications"
    - "glassbox dynamic analysis platform for malware android applications on real devices"
    - "android malware detection an eigenspace analysis approach"

Connected through hyperedge HE-6 (induced by source paper "n gram opcode analysis for android malware detection"):
  with members:
    - "android malware detection an eigenspace analysis approach"
    - "dexpler converting android dalvik bytecode to jimple for static analysis with soot"

Connected through hyperedge HE-7 (induced by source paper "n opcode analysis for android malware classification and categorization"):
  with members:
    - "android malware detection an eigenspace analysis approach"
    - "analysis of bayesian classification based approaches for android malware detection"

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)