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 "identifying dns tunneled traffic with predictive models"):
  Abstract: DNS is a distributed, fault tolerant system that avoids a single point of failure. As such it is an integral part of the internet as we use it today and hence deemed a safe protocol which is let through firewalls and proxies with no or little checks. This can be exploited by malicious agents. Network forensics is effective but struggles due to size of data and manual labour. This paper explores to what extent predictive models can be used to predict network traffic, what protocols are tunneled in the DNS protocol and more specifically whether the predictive performance is enhanced when analyzing DNS-queries and responses together and which feature set that can be used for DNS-tunneled network prediction. The tested protocols are SSH, SFTP and Telnet and the machine learning models used are Multi Layered Perceptron and Random Forests. To train the models we extract the IP Packet length, Name length and Name entropy of both the queries and responses in the DNS traffic. With an experimental research strategy it is empirically shown that the performance of the models increases when training the models on the query and respose pairs rather than using only queries or responses. The accuracy of the models is >83% and reduction in data size when features are extracted is roughly 95%. Our results provides evidence that machine learning is a valuable tool in detecting network protocols in a DNS tunnel and that only an small subset of network traffic is needed to detect this anomaly.

Includes members:
  Member "detection of malicious and low throughput data exfiltration over the dns protocol":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "maskdga a black box evasion technique against dga classifiers and adversarial defenses")
  Member "entropy based prediction of network protocols in the forensic analysis of dns tunnels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "detection of malicious and low throughput data exfiltration over the dns protocol")
  Member "detecting dns tunnels using character frequency analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a covert data transport protocol")

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)