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: "an effective approach for classification of advanced malware with high accuracy"
  Abstract: Combating malware is very important for software/systems security, but to prevent the software/systems from the advanced malware, viz. metamorphic malware is a challenging task, as it changes the structure/code after each infection. Therefore in this paper, we present a novel approach to detect the advanced malware with high accuracy by analyzing the occurrence of opcodes (features) by grouping the executables. These groups are made on the basis of our earlier studies [1] that the difference between the sizes of any two malware generated by popular advanced malware kits viz. PS-MPC, G2 and NGVCK are within 5 KB. On the basis of obtained promising features, we studied the performance of thirteen classifiers using N-fold cross-validation available in machine learning tool WEKA. Among these thirteen classifiers we studied in-depth top five classifiers (Random forest, LMT, NBT, J48 and FT) and obtain more than 96.28% accuracy for the detection of unknown malware, which is better than the maximum detection accuracy (95.9%) reported by Santos et al (2013). In these top five classifiers, our approach obtained a detection accuracy of 97.95% by the Random forest.

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"
    - "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"
    - "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"
    - "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"
    - "evolution and detection of polymorphic and metamorphic malwares a survey"

Connected through hyperedge HE-3 (induced by source paper "detection of advanced malware by machine learning techniques"):
  with members:
    - "grouping the executables to detect malware with high accuracy"
    - "novel feature extraction selection and fusion for effective malware family classification"

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:
    - "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"
    - "evolution and detection of polymorphic and metamorphic malwares a survey"

Connected through hyperedge HE-5 (induced by source paper "malware detection using machine learning and deep learning"):
  with members:
    - "an investigation of a deep learning based malware detection system"
    - "comparison of deep learning and the classical machine learning algorithm for the malware detection"
    - "deepsign deep learning for automatic malware signature generation and classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "an investigation of the classifiers to detect android malicious apps"):
  with members:
    - "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"

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)