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: "evolution and detection of polymorphic and metamorphic malwares a survey"
  Abstract: Malwares are big threat to digital world and evolving with high complexity. It can penetrate networks, steal confidential information from computers, bring down servers and can cripple infrastructures etc. To combat the threat/attacks from the malwares, anti- malwares have been developed. The existing anti-malwares are mostly based on the assumption that the malware structure does not changes appreciably. But the recent advancement in second generation malwares can create variants and hence posed a challenge to anti-malwares developers. To combat the threat/attacks from the second generation malwares with low false alarm we present our survey on malwares and its detection techniques.

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"
    - "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"
    - "improving the detection accuracy of unknown malware by partitioning the executables in groups"

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"
    - "improving the detection accuracy of unknown malware by partitioning the executables in groups"

Connected through hyperedge HE-4 (induced by source paper "comparison of deep learning and the classical machine learning algorithm for the malware detection"):
  with members:
    - "an effective approach for classification of advanced malware with high 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"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

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"
    - "improving the detection accuracy of unknown malware by partitioning the executables in groups"
    - "n opcode analysis for android malware classification and categorization"

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)