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: "malware detection with lstm using opcode language"
  Abstract: Nowadays, with the booming development of Internet and software industry, more and more malware variants are designed to perform various malicious activities. Traditional signature-based detection methods can not detect variants of malware. In addition, most behavior-based methods require a secure and isolated environment to perform malware detection, which is vulnerable to be contaminated. In this paper, similar to natural language processing, we propose a novel and efficient approach to perform static malware analysis, which can automatically learn the opcode sequence patterns of malware. We propose modeling malware as a language and assess the feasibility of this approach. First, We use the disassembly tool IDA Pro to obtain opcode sequence of malware. Then the word embedding technique is used to learn the feature vector representation of opcode. Finally, we propose a two-stage LSTM model for malware detection, which use two LSTM layers and one mean-pooling layer to obtain the feature representations of opcode sequences of malwares. We perform experiments on the dataset that includes 969 malware and 123 benign files. In terms of malware detection and malware classification, the evaluation results show our proposed method can achieve average AUC of 0.99 and average AUC of 0.987 in best case, respectively.

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)