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 "exploring non homogeneity and dynamicity of high scale cloud through hive and pig"):
  Abstract: Cloud computing deals with heterogeneity and dynamicity at all levels and therefore there is a need to manage resources in such an environment and properly allocate them. Resource planning and scheduling requires a proper understanding of arrival patterns and scheduling of resources. Study of workloads can aid in proper understanding of their associated environment. Google has released its latest version of cluster trace, trace version 2.1 in November 2014.The trace consists of cell information of about 29 days spanning across 700k jobs. This paper deals with statistical analysis of this cluster trace. Since the size of trace is very large, Hive which is a Hadoop distributed file system (HDFS) based platform for querying and analysis of Big data, has been used. Hive was accessed through its Beeswax interface. The data was imported into HDFS through HCatalog. Apart from Hive, Pig which is a scripting language and provides abstraction on top of Hadoop was used. To the best of our knowledge the analytical method adopted by us is novel and has helped in gaining several useful insights. Clustering of jobs and arrival time has been done in this paper using K-means++ clustering followed by analysis of distribution of arrival time of jobs which revealed weibull distribution while resource usage was close to zip-f like distribution and process runtimes revealed heavy tailed distribution.

Includes members:
  Member "analysis and clustering of workload in google cluster trace based on resource usage":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "resource aware min min ramm algorithm for resource allocation in cloud computing environment")
  Member "an effective framework for managing university data using a cloud based environment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cloud based big data analytics a survey of current research and future directions")
  Member "recent developments in cloud based systems state of art":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bamhealthcloud a biometric authentication and data management system for healthcare data in cloud")
  Member "an nbdmmm algorithm based framework for allocation of resources in cloud":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "resource aware min min ramm algorithm for resource allocation in cloud computing environment")
  Member "a decision matrix and monitoring based framework for infrastructure performance enhancement in a cloud based environment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploiting data reduction principles in cloud based data management for cryo image data")

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)