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: "optex a deadline aware cost optimization model for spark"
  Abstract: We present OptEx, a closed-form model of jobexecution on Apache Spark, a popular parallel processing engine. To the best of our knowledge, OptEx is the first work thatanalytically models job completion time on Spark. The model canbe used to estimate the completion time of a given Spark job ona cloud, with respect to the size of the input dataset, the numberof iterations, the number of nodes comprising the underlyingcluster. Experimental results demonstrate that OptEx yields amean relative error of 6% in estimating the job completion time. Furthermore, the model can be applied for estimating the costoptimal cluster composition for running a given Spark job ona cloud under a completion deadline specified in the SLO (i.e.,Service Level Objective). We show experimentally that OptEx isable to correctly estimate the cost optimal cluster compositionfor running a given Spark job under an SLO deadline with anaccuracy of 98%.

Connected through hyperedge HE-1 (induced by source paper "resource management and scheduling for big data applications in cloud computing environments"):
  with members:
    - "the anatomy of big data computing"
    - "big data computing and clouds"

Connected through hyperedge HE-2 (induced by source paper "a survey and taxonomy of resource optimisation for executing bag of task applications on public clouds"):
  with members:
    - "managing deadline constrained bag of tasks jobs on hybrid clouds"
    - "budget constrained execution of multiple bag of tasks applications on the cloud"
    - "executing bag of distributed tasks on virtually unlimited cloud resources"
    - "task scheduling on the cloud with hard constraints"
    - "executing bag of distributed tasks on the cloud investigating the trade offs between performance and cost"
    - "patterns in the chaos a study of performance variation and predictability in public iaas clouds"
    - "cloud benchmarking for performance"

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)