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 "comparison of deep learning and the classical machine learning algorithm for the malware detection"):
  Abstract: Recently, Deep Learning has been showing promising results in various Artificial Intelligence applications like image recognition, natural language processing, language modeling, neural machine translation, etc. Although, in general, it is computationally more expensive as compared to classical machine learning techniques, their results are found to be more effective in some cases. Therefore, in this paper, we investigated and compared one of the Deep Learning Architecture called Deep Neural Network (DNN) with the classical Random Forest (RF) machine learning algorithm for the malware classification. We studied the performance of the classical RF and DNN with 2, 4 & 7 layers architectures with the four different feature sets, and found that irrespective of the features inputs, the classical RF accuracy outperforms the DNN.

Includes members:
  Member "an effective approach for classification of advanced malware with high accuracy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on the detection of android malicious apps")
  Member "a multi task learning model for malware classification with useful file access pattern from api call sequence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an investigation of a deep learning based malware detection system")
  Member "deepsign deep learning for automatic malware signature generation and classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a roadmap toward the resilient internet of things for cyber physical systems")
  Member "novel feature extraction selection and fusion for effective malware family classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning from context exploiting and interpreting file path information for better malware detection")
  Member "deep neural network based malware detection using two dimensional binary program features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "fast and accurate deep network learning by exponential linear units elus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "evolution and detection of polymorphic and metamorphic malwares a survey":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on the detection of android malicious apps")

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)