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: "provisioning spot market cloud resources to create cost effective virtual clusters"
  Abstract: Infrastructure-as-a-Service providers are offering their unused resources in the form of variable-priced virtual machines (VMs), known as "spot instances", at prices significantly lower than their standard fixed-priced resources. To lease spot instances, users specify a maximum price they are willing to pay per hour and VMs will run only when the current price is lower than the user's bid. This paper proposes a resource allocation policy that addresses the problem of running deadlineconstrained compute-intensive jobs on a pool of composed solely of spot instances, while exploiting variations in price and performance to run applications in a fast and economical way. Our policy relies on job runtime estimations to decide what are the best types of VMs to run each job and when jobs should run. Several estimation methods are evaluated and compared, using trace-based simulations, which take real price variation traces obtained from Amazon Web Services as input, as well as an application trace from the Parallel Workload Archive. Results demonstrate the effectiveness of running computational jobs on spot instances, at a fraction (up to 60% lower) of the price that would normally cost on fixed priced resources.

Connected through hyperedge HE-1 (induced by source paper "an efficient cloud scheduler design supporting preemptible instances"):
  with members:
    - "resource provisioning in science clouds requirements and challenges"
    - "improved cloud resource allocation how indigo datacloud is overcoming the current limitations in cloud schedulers"
    - "efficient image deployment in cloud environments"
    - "application centric resource provisioning for amazon ec2 spot instances"
    - "reliable provisioning of spot instances for compute intensive applications"
    - "cloud computing and grid computing 360 degree compared"

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"
    - "phenomenology tools on cloud infrastructures using openstack"
    - "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"
    - "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 "spot pricing in the cloud ecosystem a comparative investigation"):
  with members:
    - "on evaluating commercial cloud services a systematic review"
    - "application centric resource provisioning for amazon ec2 spot instances"
    - "reliable provisioning of spot instances for compute intensive applications"
    - "cloud computing and grid computing 360 degree compared"

Connected through hyperedge HE-4 (induced by source paper "reliable provisioning of spot instances for compute intensive applications"):
  with members:
    - "modeling and simulation of scalable cloud computing environments and the cloudsim toolkit challenges and opportunities"

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)