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 "malware classification using deep learning based feature extraction and wrapper based feature selection technique"):
  Abstract: In case of behavior analysis of a malware, categorization of malicious files is an essential part after malware detection. Numerous static and dynamic techniques have been reported so far for categorizing malwares. This research work presents a deep learning based malware detection (DLMD) technique based on static methods for classifying different malware families. The proposed DLMD technique uses both the byte and ASM files for feature engineering and thus classifying malwares families. First, features are extracted from byte files using two different types of Deep Convolutional Neural Networks (CNN). After that, important and discriminative opcode features are selected using a wrapper-based mechanism, where Support Vector Machine (SVM) is used as a classifier. The idea is to construct a hybrid feature space by combining the different feature spaces in order that the shortcoming of a particular feature space may be overcome by another feature space. And consequently to reduce the chances of missing a malware. Finally, the hybrid feature space is then used to train a Multilayer Perceptron, which classifies all the nine different malware families. Experimental results show that proposed DLMD technique achieves log-loss of 0.09 for ten independent runs. Moreover, the performance of the proposed DLMD technique is compared against different classifiers and shows its effectiveness in categorizing malwares. The relevant code and database can be found at this https URL.

Includes members:
  Member "detecting and classifying android malware using static analysis along with creator information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "copycat practical adversarial attacks on visualization based malware detection")
  Member "a survey of the recent architectures of deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a precisely xtreme multi channel hybrid approach for roman urdu sentiment analysis")
  Member "adversarial deep learning for robust detection of binary encoded malware":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "effectiveness of adversarial examples and defenses for malware classification")
  Member "deepsign deep learning for automatic malware signature generation and classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "learning from context exploiting and interpreting file path information for better malware detection")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "malware classification based on call graph clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "new era of deeplearning based malware intrusion detection the malware detection and prediction based on deep learning")

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)