human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "integration of static and dynamic analysis for malware family classification with composite neural network"):
  Abstract: Deep learning has been used in the research of malware analysis. Most classification methods use either static analysis features or dynamic analysis features for malware family classification, and rarely combine them as classification features and also no extra effort is spent integrating the two types of features. In this paper, we combine static and dynamic analysis features with deep neural networks for Windows malware classification. We develop several methods to generate static and dynamic analysis features to classify malware in different ways. Given these features, we conduct experiments with composite neural network, showing that the proposed approach performs best with an accuracy of 83.17% on a total of 80 malware families with 4519 malware samples. Additionally, we show that using integrated features for malware family classification outperforms using static features or dynamic features alone. We show how static and dynamic features complement each other for malware classification.

Includes members:
  Member "theoretical investigation of composite neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "composite neural network theory and application to pm2 5 prediction")
  Member "behavioral malware classification using convolutional recurrent neural networks":
    also connected through other hyperedges:
      (none)
  Member "a family of droids android malware detection via behavioral modeling static vs dynamic analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mamadroid detecting android malware by building markov chains of behavioral models extended version")
  Member "robust neural malware detection models for emulation sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "defense methods against adversarial examples for recurrent neural networks")
  Member "attack and defense of dynamic analysis based adversarial neural malware classification models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "virtual machine introspection based malware behavior profiling and family grouping":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning malware representation based on execution sequences")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "dynamic analysis of executables to detect and characterize malware":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "detecting malicious powershell commands using deep neural networks")

Question: Please tell me which class the source paper 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)