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: "straggler mitigation at scale"
  Abstract: Runtime performance variability at the servers has been a major issue, hindering the predictable and scalable performance in modern distributed systems. Executing requests or jobs redundantly over multiple servers has been shown to be effective for mitigating variability, both in theory and practice. Systems that employ redundancy has drawn significant attention, and numerous papers have analyzed the pain and gain of redundancy under various service models and assumptions on the runtime variability. This paper presents a cost (pain) vs. latency (gain) analysis of executing jobs of many tasks by employing replicated or erasure coded redundancy. Tail heaviness of service time variability is decisive on the pain and gain of redundancy and we quantify its effect by deriving expressions for the cost and latency. Specifically, we try to answer four questions: 1) How do replicated and coded redundancy compare in the cost vs. latency tradeoff? 2) Can we introduce redundancy after waiting some time and expect to reduce the cost? 3) Can relaunching the tasks that appear to be straggling after some time help to reduce cost and/or latency? 4) Is it effective to use redundancy and relaunching together? We validate the answers we found for each of the questions via simulations that use empirical distributions extracted from a Google cluster data.

Connected through hyperedge HE-1 (induced by source paper "timely distributed computation with stragglers"):
  with members:
    - "trading off computation with transmission in status update systems"
    - "age of information with prioritized streams when to buffer preempted packets"
    - "sampling for remote estimation through queues age of information and beyond"
    - "computation scheduling for distributed machine learning with straggling workers"
    - "age of information for computation intensive messages in mobile edge computing"
    - "age of information for discrete time queues"
    - "on timely channel coding with hybrid arq"
    - "mds coding is better than replication for job completion times"

Connected through hyperedge HE-2 (induced by source paper "analyzing the download time of availability codes"):
  with members:
    - "simplex queues for hot data download"
    - "on the service capacity region of accessing erasure coded content"
    - "rate optimal binary linear locally repairable codes with small availability"
    - "efficient redundancy techniques for latency reduction in cloud systems"
    - "provably delay efficient data retrieving in storage clouds"
    - "repair locality with multiple erasure tolerance"
    - "locality and availability in distributed storage"
    - "bounds on locally recoverable codes with multiple recovering sets"

Connected through hyperedge HE-3 (induced by source paper "redundancy scheduling in systems with bi modal job service time distribution"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "on the effect of task to worker assignment in distributed computing systems with stragglers"
    - "straggler mitigation by delayed relaunch of tasks"
    - "effective straggler mitigation which clones should attack and when"
    - "efficient redundancy techniques for latency reduction in cloud systems"
    - "on the delay storage trade off in content download from coded distributed storage systems"

Connected through hyperedge HE-4 (induced by source paper "scheduling in the presence of data intensivecompute jobs"):
  with members:
    - "redundancy scheduling in systems with bi modal job service time distribution"
    - "on the effect of task to worker assignment in distributed computing systems with stragglers"
    - "on the delay storage trade off in content download from coded distributed storage systems"

Connected through hyperedge HE-5 (induced by source paper "data replication for reducing computing time in distributed systems with stragglers"):
  with members:
    - "on the effect of task to worker assignment in distributed computing systems with stragglers"
    - "coding for fast content download"

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)