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: "power consumption of virtualization technologies an empirical investigation"
  Abstract: Virtualization is growing rapidly as a result of the increasing number of alternative solutions in this area, and of the wide range of application field. Until now, hypervisor-based virtualization has been the de facto solution to perform server virtualization. Recently, container-based virtualization -- an alternative to hypervisors -- has gained more attention because of lightweight characteristics, attracting cloud providers that have already made use of it to deliver their services. However, a gap in the existing research on containers exists in the area of power consumption. This paper presents the results of a performance comparison in terms of power consumption of four different virtualization technologies: KVM and Xen, which are based on hypervisor virtualization, Docker and LXC which are based on container virtualization. The aim of this empirical investigation, carried out by means of a testbed, is to understand how these technologies react to particular workloads. Our initial results show how, despite of the number of virtual entities running, both kinds of virtualization alternatives behave similarly in idle state and in CPU/Memory stress test. Contrarily, the results on network performance show differences between the two technologies.

Connected through hyperedge HE-1 (induced by source paper "management and orchestration of virtual network functions via deep reinforcement learning"):
  with members:
    - "deep reinforcement learning with discrete normalized advantage functions for resource management in network slicing"
    - "machine learning in the air"
    - "deep reinforcement learning for resource management in network slicing"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "auto scaling network resources using machine learning to improve qos and reduce cost"
    - "addressing function approximation error in actor critic methods"
    - "deep learning in mobile and wireless networking a survey"

Connected through hyperedge HE-2 (induced by source paper "dynamic cloud network control under reconfiguration delay and cost"):
  with members:
    - "optimal dynamic cloud network control"
    - "throughput optimal scheduling for multi hop networked transportation systems with switch over delay"
    - "optimal control of wireless computing networks"
    - "service function chaining resource allocation a survey"
    - "streaming big data meets backpressure in distributed network computation"
    - "adaptive policies for scheduling with reconfiguration delay an end to end solution for all optical data centers"

Connected through hyperedge HE-3 (induced by source paper "auto scaling network resources using machine learning to improve qos and reduce cost"):
  with members:
    - "on service chaining strategies using virtual network functions in operator networks"
    - "design and analysis of deadline and budget constrained autoscaling dbca algorithm for 5g mobile networks"

Connected through hyperedge HE-4 (induced by source paper "a comparative study of containers and virtual machines in big data environment"):
  with members:
    - "a performance evaluation of container technologies on internet of things devices"
    - "analysis of docker security"

Connected through hyperedge HE-5 (induced by source paper "characterizing the power cost of virtualization environments"):
  with members:
    - "characterizing docker overhead in mobile edge computing scenarios"

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)