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 comparison of delivery and coding policies in data clusters"):
  Abstract: A key function of cloud infrastructure is to store and deliver diverse files, e.g., scientific datasets, social network information, videos, etc. In such systems, for the purpose of fast and reliable delivery, files are divided into chunks, replicated or erasure-coded, and disseminated across servers. It is neither known in general how delays scale with the size of a request nor how delays compare under different policies for coding, data dissemination, and delivery. #R##N#Motivated by these questions, we develop and explore a set of evolution equations as a unified model which captures the above features. These equations allow for both efficient simulation and mathematical analysis of several delivery policies under general statistical assumptions. In particular, we quantify in what sense a workload aware delivery policy performs better than a workload agnostic policy. Under a dynamic or stochastic setting, the sample path comparison of these policies does not hold in general. The comparison is shown to hold under the weaker increasing convex stochastic ordering, still stronger than the comparison of averages. #R##N#This result further allows us to obtain insightful computable performance bounds. For example, we show that in a system where files are divided into chunks of equal size, replicated or erasure-coded, and disseminated across servers at random, the job delays increase sub-logarithmically in the request size for small and medium-sized files but linearly for large files.

Includes members:
  Member "tofec achieving optimal throughput delay trade off of cloud storage using erasure codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal load allocation for coded distributed computation in heterogeneous clusters")
  Member "on the delay storage trade off in content download from coded distributed storage systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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 "the mds queue analysing the latency performance of erasure codes":
    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")

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)