human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "scalability of vm provisioning systems"
  Abstract: Virtual machines and virtualized hardware have been around for over half a century. The commoditization of the x86 platform and its rapidly growing hardware capabilities have led to recent exponential growth in the use of virtualization both in the enterprise and high performance computing (HPC). The startup time of a virtualized environment is a key performance metric for high performance computing in which the runtime of any individual task is typically much shorter than the lifetime of a virtualized service in an enterprise context. In this paper, a methodology for accurately measuring the startup performance on an HPC system is described. The startup performance overhead of three of the most mature, widely deployed cloud management frameworks (OpenStack, OpenNebula, and Eucalyptus) is measured to determine their suitability for workloads typically seen in an HPC environment. A 10x performance difference is observed between the fastest (Eucalyptus) and the slowest (OpenNebula) framework. This time difference is primarily due to delays in waiting on networking in the cloud-init portion of the startup. The methodology and measurements presented should facilitate the optimization of startup across a variety of virtualization environments.

Connected through hyperedge HE-1 (induced by source paper "resource sharing policy in multi tenant scientific workflow as a service platform"):
  with members:
    - "task runtime prediction in scientific workflows using an online incremental learning approach"
    - "patterns in the chaos a study of performance variation and predictability in public iaas clouds"

Connected through hyperedge HE-2 (induced by source paper "interactive launch of 16 000 microsoft windows instances on a supercomputer"):
  with members:
    - "the lustre storage architecture"
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "scalable system scheduling for hpc and big data"
    - "llmapreduce multi level map reduce for high performance data analysis"
    - "scheduler technologies in support of high performance data analysis"
    - "enabling on demand database computing with mit supercloud database management system"
    - "big data strategies for data center infrastructure management using a 3d gaming platform"
    - "d4m bringing associative arrays to database engines"

Connected through hyperedge HE-3 (induced by source paper "multiple workflows scheduling in multi tenant distributed systems a taxonomy and future directions"):
  with members:
    - "predictive performance modeling for distributed batch processing using black box monitoring and machine learning"
    - "container solutions for hpc systems a case study of using shifter on blue waters"
    - "task runtime prediction in scientific workflows using an online incremental learning approach"
    - "patterns in the chaos a study of performance variation and predictability in public iaas clouds"
    - "monetary cost optimizations for hosting workflow as a service in iaas clouds"
    - "internet of things iot a vision architectural elements and future directions"
    - "a taxonomy and survey of energy efficient data centers and cloud computing systems"
    - "a taxonomy of workflow management systems for grid computing"

Connected through hyperedge HE-4 (induced by source paper "interactive supercomputing on 40 000 cores for machine learning and data analysis"):
  with members:
    - "benchmarking data analysis and machine learning applications on the intel knl many core processor"
    - "tensorflow a system for large scale machine learning"
    - "enabling on demand database computing with mit supercloud database management system"
    - "d4m bringing associative arrays to database engines"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "julia a fresh approach to numerical computing"
    - "computing on masked data a high performance method for improving big data veracity"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"

Connected through hyperedge HE-5 (induced by source paper "lessons learned from a decade of providing interactive on demand high performance computing to scientists and engineers"):
  with members:
    - "mit supercloud portal workspace enabling hpc web application deployment"
    - "enabling on demand database computing with mit supercloud database management system"
    - "big data strategies for data center infrastructure management using a 3d gaming platform"
    - "d4m bringing associative arrays to database engines"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "computing on masked data a high performance method for improving big data veracity"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"

Question: Please tell me which class the center node 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)