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: "phenomenology tools on cloud infrastructures using openstack"
  Abstract: We present a new environment for computations in particle physics phenomenology employing recent developments in cloud computing. On this environment users can create and manage “virtual” machines on which the phenomenology codes/tools can be deployed easily in an automated way. We analyze the performance of this environment based on “virtual” machines versus the utilization of physical hardware. In this way we provide a qualitative result for the influence of the host operating system on the performance of a representative set of applications for phenomenology calculations.

Connected through hyperedge HE-1 (induced by source paper "orchestrating complex application architectures in heterogeneous clouds"):
  with members:
    - "improved cloud resource allocation how indigo datacloud is overcoming the current limitations in cloud schedulers"
    - "standards for enabling heterogeneous iaas cloud federations"
    - "indigo datacloud foundations and architectural description of a platform as a service oriented to scientific computing"
    - "analysis of scientific cloud computing requirements"

Connected through hyperedge HE-2 (induced by source paper "resource provisioning in science clouds requirements and challenges"):
  with members:
    - "the anatomy of big data computing"
    - "patience aware scheduling for cloud services freeing users from the chains of boredom"
    - "maximizing cloud providers revenues via energy aware allocation policies"
    - "reliable provisioning of spot instances for compute intensive applications"
    - "workload classification a software energy measurement for efficient scheduling on private cloud platforms"
    - "provisioning spot market cloud resources to create cost effective virtual clusters"
    - "energy efficient management of data center resources for cloud computing a vision architectural elements and open challenges"
    - "cloud computing and grid computing 360 degree compared"

Connected through hyperedge HE-3 (induced by source paper "analysis of scientific cloud computing requirements"):
  with members:
    - "an architecture for integrated intelligence in urban management using cloud computing"
    - "behavioral simulations in mapreduce"

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)