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 "optimizing redundancy levels in master worker compute clusters for straggler mitigation"):
  Abstract: Runtime variability in computing systems causes some tasks to straggle and take much longer than expected to complete. These straggler tasks are known to significantly slowdown distributed computation. Job execution with speculative execution of redundant tasks has been the most widely deployed technique for mitigating the impact of stragglers, and many recent theoretical papers have studied the advantages and disadvantages of using redundancy under various system and service models. However, no clear guidelines could yet be found on when, for which jobs, and how much redundancy should be employed in Master-Worker compute clusters, which is the most widely adopted architecture in modern compute systems. We are concerned with finding a strategy for scheduling jobs with redundancy that works well in practice. This is a complex optimization problem, which we address in stages. We first use Reinforcement Learning (RL) techniques to learn good scheduling principles from realistic experience. Building on these principles, we derive a simple scheduling policy and present an approximate analysis of its performance. Specifically, we derive expressions to decide when and which jobs should be scheduled with how much redundancy. We show that policy that we devise in this way performs as good as the more complex policies that are derived by RL. Finally, we extend our approximate analysis to the case when system employs the other widely deployed remedy for stragglers, which is relaunching straggler tasks after waiting some time. We show that scheduling with redundancy significantly outperforms straggler relaunch policy when the offered load on the system is low or moderate, and performs slightly worse when the offered load is very high.

Includes members:
  Member "scheduling with predictions and the price of misprediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online metric algorithms with untrusted predictions")
  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 "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "bigroots an effective approach for root cause analysis of stragglers in big data system":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "straggler mitigation at scale")
  Member "straggler mitigation by delayed relaunch of tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "codenet training large scale neural networks in presence of soft errors")
  Member "near optimal straggler mitigation for distributed gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a locality based approach for coded computation")
  Member "a machine learning approach to routing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wield systematic reinforcement learning with progressive randomization")
  Member "coded convolution for parallel and distributed computing within a deadline":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")

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)