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 "covert channel detection framework for cloud using distributed machine learning"):
  Abstract: Recent research shows that colluded malware in different VMs sharing physical host may use access latency of a resource as a covert channel to leak critical information. Covert channels employ time characteristics to transmit confidential information to attackers. In this manuscript we have made two important contributions and to the best of our knowledge they are novel. One is to propose a framework for detecting covert channel attack based anomalies in cloud using machine learning . This framework is based on application criticality where the degree of error tolerance will be based on the sensitivity of application. This is a useful feature for cloud based applications as SLAs are trade off between service and cost. There are two modules of the framework, a cluster monitor and another module becomes part of hypervisor for monitoring purposes. Second contribution is we propose an approach to distribute the machine learning technique in such a manner to be able to handle imbalance learning as well as enabling SVM to handle large datasets.

Includes members:
  Member "seeking black lining in cloud":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "detection of information leakage in cloud")
  Member "determinating timing channels in compute clouds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "identifying cache based side channels through secret augmented abstract interpretation")
  Member "covert channel detection using information theory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fundamental limits of covert packet insertion")

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.DC(Distributed, Parallel, and Cluster Computing)