human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "using straggler replication to reduce latency in large scale parallel computing extended version"):
  Abstract: Users expect fast and fluid response from today's cloud infrastructure. Large-scale computing frameworks such as MapReduce divide jobs into many parallel tasks and execute them on different machines to enable faster processing. But the tasks on the slowest machines (straggling tasks) become the bottleneck in the completion of the job. One way to combat the variability in machine response time, is to add replicas of straggling tasks and wait for one copy to finish. #R##N#In this paper we analyze how task replication strategies can be used to reduce latency, and their impact on the cost of computing resources. We use extreme value theory to show that the tail of the execution time distribution is the key factor in characterizing the trade-off between latency and computing cost. From this trade-off we can determine which task replication strategies reduce latency, without a large increase in computing cost. We also propose a heuristic algorithm to search for the best replication strategies when it is difficult to fit a simple distribution to model the empirical behavior of task execution time, and use the proposed analysis techniques. Evaluation of the heuristic policies on Google Trace data shows a significant latency reduction compared to the replication strategy used in MapReduce.

Includes members:
  Member "low latency via redundancy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient task replication for fast response times in parallel computation")
  Member "when do redundant requests reduce latency":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "straggler resistant distributed matrix computation via coding theory")
  Member "on the delay storage trade off in content download from coded distributed storage systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a combinatorial view of the service rates of codes problem its equivalence to fractional matching and its connection with batch codes")
  Member "coding for fast content download":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a combinatorial view of the service rates of codes problem its equivalence to fractional matching and its connection with batch codes")

Question: Please tell me which class the source paper 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)