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: "improving the detection accuracy of unknown malware by partitioning the executables in groups"
  Abstract: Detection of unknown malware with high accuracy is always a challenging task. Therefore, in this paper we study the classification of unknown malware by two methods. In the first/regular method, similar to other authors (Mehdi et al. Proceedings of the 11th Annual conference on Genetic and evolutionary computation 2009, Moskovitch et al. Intelligence and Security Informatics 2008, Ravi and Manoharan Int J Comput Appl 43(17):12–16 2012) approaches we select the features by taking all dataset in one group and in second method, we select the features by partitioning the dataset in the range of file 5 KB size. We find that the second method detect the malwares with ~8.7 % more accurate than the first/regular method.

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"
    - "n opcode analysis for android malware classification and categorization"
    - "evolution and detection of polymorphic and metamorphic malwares a survey"
    - "droidanalytics a signature based analytic system to collect extract analyze and associate android malware"

Connected through hyperedge HE-2 (induced by source paper "an efficient detection of malware by naive bayes classifier using gpgpu"):
  with members:
    - "group wise classification approach to improve android malicious apps detection accuracy"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "microsoft malware classification challenge"
    - "an effective approach for classification of advanced malware with high accuracy"
    - "acceleration of statistical detection of zero day malware in the memory dump using cuda enabled gpu hardware"
    - "evolution and detection of polymorphic and metamorphic malwares a survey"

Connected through hyperedge HE-3 (induced by source paper "group wise classification approach to improve android malicious apps detection accuracy"):
  with members:
    - "an investigation of the classifiers to detect android malicious apps"
    - "adaptive and scalable android malware detection through online learning"
    - "evolution and detection of polymorphic and metamorphic malwares a survey"

Connected through hyperedge HE-4 (induced by source paper "an investigation of a deep learning based malware detection system"):
  with members:
    - "group wise classification approach to improve android malicious apps detection accuracy"
    - "a multi task learning model for malware classification with useful file access pattern from api call sequence"
    - "deepsign deep learning for automatic malware signature generation and classification"
    - "novel feature extraction selection and fusion for effective malware family classification"
    - "deep neural network based malware detection using two dimensional binary program features"

Connected through hyperedge HE-5 (induced by source paper "an investigation of the classifiers to detect android malicious apps"):
  with members:
    - "an effective approach for classification of advanced malware with high accuracy"
    - "n opcode analysis for android malware classification and categorization"
    - "evolution and detection of polymorphic and metamorphic malwares a survey"

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)