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 "a compression based framework for the detection of anomalies in heterogeneous data sources"):
  Abstract: Nowadays, information and communications technology systems are fundamental assets of our social and economical model, and thus they should be properly protected against the malicious activity of cybercriminals. Defence mechanisms are generally articulated around tools that trace and store information in several ways, the simplest one being the generation of plain text files coined as security logs. This log files are usually inspected, in a semi-automatic way, by security analysts to detect events that may affect system integrity. On this basis, we propose a parameter-free methodology to detect security incidents from structured text regardless its nature. We use the Normalized Compression Distance to obtain a set of features that can be used by a Support Vector Machine to classify events from a heterogeneous cybersecurity environment. In specific, we explore and validate the application of our methodology in four different cybersecurity domains: HTTP anomaly identification, spam detection, Domain Generation Algorithms tracking and sentiment analysis. The results obtained show the validity and flexibility of our approach in different security scenarios with a low configuration burden.

Includes members:
  Member "an adaptive system for detecting malicious queries in web attacks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the threat of adversarial attacks on machine learning in network security a survey")
  Member "predicting cyber events by leveraging hacker sentiment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "characterizing activity on the deep and dark web")
  Member "shallow and deep networks intrusion detection system a taxonomy and survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "autoids auto encoder based method for intrusion detection system")
  Member "automatic detection of malware generated domains with recurrent neural models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "charbot a simple and effective method for evading dga classifiers")
  Member "current challenges and future research areas for digital forensic investigation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "methodology for the automated metadata based classification of incriminating digital forensic artefacts")
  Member "predicting domain generation algorithms with long short term memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nested multiple instance learning in modelling of http network traffic")

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)