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 "delay optimal policies in partial fork join systems with redundancy and random slowdowns"):
  Abstract: We consider a large distributed service system consisting of $n$ homogeneous servers with infinite capacity FIFO queues. Jobs arrive as a Poisson process of rate $\lambda n/k_n$ (for some positive constant $\lambda$ and integer $k_n$). Each incoming job consists of $k_n$ identical tasks that can be executed in parallel, and that can be encoded into at least $k_n$ "replicas" of the same size (by introducing redundancy) so that the job is considered to be completed when any $k_n$ replicas associated with it finish their service. Moreover, we assume that servers can experience random slowdowns in their processing rate so that the service time of a replica is the product of its size and a random slowdown. #R##N#First, we assume that the server slowdowns are shifted exponential and independent of the replica sizes. In this setting we show that the delay of a typical job is asymptotically minimized (as $n\to\infty$) when the number of replicas per task is a constant that only depends on the arrival rate $\lambda$, and on the expected slowdown of servers. #R##N#Second, we introduce a new model for the server slowdowns in which larger tasks experience less variable slowdowns than smaller tasks. In this setting we show that, under the class of policies where all replicas start their service at the same time, the delay of a typical job is asymptotically minimized (as $n\to\infty$) when the number of replicas per task is made to depend on the actual size of the tasks being replicated, with smaller tasks being replicated more than larger tasks.

Includes members:
  Member "mds coding is better than replication for job completion times":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "timely distributed computation with stragglers")
  Member "delay comparison of delivery and coding policies in data clusters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "collaborative uploading in heterogeneous networks optimal and adaptive strategies")
  Member "on delay optimal scheduling in queueing systems with replications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploiting computation replication for mobile edge computing a fundamental computation communication tradeoff study")
  Member "contrasting effects of replication in parallel systems from overload to underload and back":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "task replication for deadline constrained vehicular cloud computing optimal policy performance analysis and implications on road traffic")
  Member "efficient redundancy techniques for latency reduction in cloud systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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 "using straggler replication to reduce latency in large scale parallel computing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "codenet training large scale neural networks in presence of soft errors")
  Member "efficient task replication for fast response times in parallel computation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "codenet training large scale neural networks in presence of soft errors")
  Member "when queueing meets coding optimal latency data retrieving scheme in storage clouds":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "differentiated latency in data center networks with erasure coded files through traffic engineering")

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.PF(Performance)