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: "android malware detection an eigenspace analysis approach"
  Abstract: The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method.

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"
    - "automated test input generation for android are we there yet"
    - "high accuracy android malware detection using ensemble learning"

Connected through hyperedge HE-2 (induced by source paper "ml fv heartsuit a survey on the application of machine learning to formal verification"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "deep network guided proof search"
    - "deepmath deep sequence models for premise selection"
    - "end to end learning for self driving cars"
    - "machine learner for automated reasoning 0 4 and 0 5"
    - "a bayesian approach to tackling hard computational problems"

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"
    - "automated test input generation for android are we there yet"
    - "high accuracy android malware detection using ensemble learning"

Connected through hyperedge HE-4 (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"
    - "high accuracy android malware detection using ensemble learning"

Connected through hyperedge HE-5 (induced by source paper "dynalog an automated dynamic analysis framework for characterizing android applications"):
  with members:
    - "enter sandbox android sandbox comparison"
    - "analysis of bayesian classification based approaches for android malware detection"
    - "a new android malware detection approach using bayesian classification"
    - "ananas a framework for analyzing android applications"

Connected through hyperedge HE-6 (induced by source paper "n gram opcode analysis for android malware detection"):
  with members:
    - "high accuracy android malware detection using ensemble learning"
    - "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:
    - "high accuracy android malware detection using ensemble learning"
    - "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)