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: "on the optimality of scheduling dependent mapreduce tasks on heterogeneous machines"
  Abstract: MapReduce is the most popular big-data computation framework, motivating many research topics. A MapReduce job consists of two successive phases, i.e. map phase and reduce phase. Each phase can be divided into multiple tasks. A reduce task can only start when all the map tasks finish processing. A job is successfully completed when all its map and reduce tasks are complete. The task of optimally scheduling the different tasks on different servers to minimize the weighted completion time is an open problem, and is the focus of this paper. In this paper, we give an approximation ratio with a competitive ratio $2(1+(m-1)/D)+1$, where $m$ is the number of servers and $D\ge 1$ is the task-skewness product. We implement the proposed algorithm on Hadoop framework, and compare with three baseline schedulers. Results show that our DMRS algorithm can outperform baseline schedulers by up to $82\%$.

Connected through hyperedge HE-1 (induced by source paper "resource allocation in multigranular optical networks"):
  with members:
    - "differentiated latency in data center networks with erasure coded files through traffic engineering"
    - "a reconfigurable high performance optical data center architecture"
    - "chronos a unifying optimization framework for speculative execution of deadline critical mapreduce jobs"
    - "joint latency and cost optimization for erasure coded data center storage"
    - "sprout a functional caching approach to minimize service latency in erasure coded storage"

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)