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 "redundancy scheduling in systems with bi modal job service time distribution"):
  Abstract: Queuing systems with redundant requests have drawn great attention because of their promise to reduce the job completion time and its variability. Despite a large body of work on this topic, we are still far from fully understanding the benefits of redundancy in practice. We here take one step towards practical systems by studying queuing systems with bi-modal job service time distribution. Such distributions have been observed in practical systems, as can be seen in Google cluster traces. We develop an analogy to a classical urns and balls problem, and use it to study the queuing time performance of two non-adaptive classical scheduling policies: random and round-robin. In particular, we introduce new performance metrics in the analogous model, and argue that they are good indicators of the queuing time performance of non-adaptive scheduling policies. We then propose a new non-adaptive scheduling policy that is based on combinatorial designs, and show that it improves our performance metrics. We observe in simulation that the proposed scheduling policy, as the performance metrics indicate, reduces the queuing times up to 25% compared to random scheduling and up to 100% compared round-robin scheduling.

Includes members:
  Member "straggler mitigation at scale":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "timely distributed computation with stragglers")
  Member "speeding up distributed machine learning using codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "on the effect of task to worker assignment in distributed computing systems with stragglers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "age of information for updates with distortion constant and age dependent distortion constraints")
  Member "straggler mitigation by delayed relaunch of tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "codenet training large scale neural networks in presence of soft errors")
  Member "effective straggler mitigation which clones should attack and when":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "codenet training large scale neural networks in presence of soft errors")
  Member "efficient redundancy techniques for latency reduction in cloud systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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 "on the delay storage trade off in content download from coded distributed storage systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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.PF(Performance)