human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "delay optimal policies in partial fork join systems with redundancy and random slowdowns"):
  with members:
    - "mds coding is better than replication for job completion times"
    - "on delay optimal scheduling in queueing systems with replications"
    - "contrasting effects of replication in parallel systems from overload to underload and back"
    - "efficient redundancy techniques for latency reduction in cloud systems"
    - "using straggler replication to reduce latency in large scale parallel computing"
    - "efficient task replication for fast response times in parallel computation"
    - "when queueing meets coding optimal latency data retrieving scheme in storage clouds"
    - "when do redundant requests reduce latency"

Connected through hyperedge HE-2 (induced by source paper "collaborative uploading in heterogeneous networks optimal and adaptive strategies"):
  with members:
    - "a generalized performance evaluation framework for parallel systems with output synchronization"
    - "optimizing stochastic scheduling in fork join queueing models bounds and applications"
    - "efficient redundancy techniques for latency reduction in cloud systems"
    - "non asymptotic delay bounds for k l fork join systems and multi stage fork join networks"
    - "projection onto the probability simplex an efficient algorithm with a simple proof and an application"
    - "on the flow level delay of a spatial multiplexing mimo wireless channel"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-3 (induced by source paper "asymptotic response time analysis for multi task parallel jobs"):
  with members:
    - "joint latency and cost optimization for erasure coded data center storage"
    - "when do redundant requests reduce latency"
    - "on the delay storage trade off in content download from coded distributed storage systems"
    - "coding for fast content download"
    - "interactive analytical processing in big data systems a cross industry study of mapreduce workloads"

Connected through hyperedge HE-4 (induced by source paper "delay asymptotics and bounds for multi task parallel jobs"):
  with members:
    - "joint latency and cost optimization for erasure coded data center storage"
    - "when do redundant requests reduce latency"
    - "interactive analytical processing in big data systems a cross industry study of mapreduce workloads"
    - "the mds queue analysing the latency performance of erasure codes"

Question: Please tell me which class the center node 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)