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 "capturing the symptoms of malicious code in electronic documents by file s entropy signal combined with machine learning"):
  Abstract: Abstract-Email cyber-attacks based on malicious documents have become the popular techniques in today's sophisticated attacks. In the past, persistent efforts have been made to detect such attacks. But there are still some common defects in the existing methods including unable to capture unknown attacks, high overhead of resource and time, and just can be used to detect specific formats of documents. In this study, a new Framework named ESRMD (Entropy signal Reflects the Malicious document) is proposed, which can detect malicious document based on the entropy distribution of the file. In essence, ESRMD is a machine learning classifier. What makes it distinctive is that it extracts global and structural entropy features from the entropy of the malicious documents rather than the structural data or metadata of the file, enduing it the ability to deal with various document formats and against the parser-confusion and obfuscated attacks. In order to assess the validity of the model, we conducted extensive experiments on a collected dataset with 10381 samples in it, which contains malware (51.47%) and benign (48.53%) samples. The results show that our model can achieve a good performance on the true positive rate, precision and ROC with the value of 96.00%, 96.69% and 99.2% respectively. We also compared ESRMD with some leading antivirus engines and prevalent tools. The results showed that our framework can achieve a better performance compared with these engines and tools.

Includes members:
  Member "henet a deep learning approach on intel circledr processor trace for effective exploit detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "to believe or not to believe validating explanation fidelity for dynamic malware analysis")
  Member "evading classifiers by morphing in the dark":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "wavelet decomposition of software entropy reveals symptoms of malicious code":
    also connected through other hyperedges:
      (none)
  Member "bag of words representation for biomedical time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ts chief a scalable and accurate forest algorithm for time series classification")

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.CY(Computers and Society)