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 by delayed relaunch of tasks"
  Abstract: Redundancy for straggler mitigation, originally in data download and more recently in distributed computing context, has been shown to be effective both in theory and practice. Analysis of systems with redundancy has drawn significant attention and numerous papers have studied pain and gain of redundancy under various service models and assumptions on the straggler characteristics. We here present a cost (pain) vs. latency (gain) analysis of using simple replication or erasure coding for straggler mitigation in executing jobs with many tasks. We quantify the effect of the tail of task execution times and discuss tail heaviness as a decisive parameter for the cost and latency of using redundancy. Specifically, we find that coded redundancy achieves better cost vs. latency tradeoff than simple replication and can yield reduction in both cost and latency under less heavy tailed execution times. We show that delaying redundancy is not effective in reducing cost and that delayed relaunch of stragglers can yield significant reduction in cost and latency. We validate these observations by comparing with the simulations that use empirical distributions extracted from Google cluster data.

Connected through hyperedge HE-1 (induced by source paper "codenet training large scale neural networks in presence of soft errors"):
  with members:
    - "accumulation bit width scaling for ultra low precision training of deep networks"
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "hierarchical coded computation"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "straggler resilient and communication efficient distributed iterative linear solver"
    - "training deep neural networks with 8 bit floating point numbers"

Connected through hyperedge HE-2 (induced by source paper "grand challenges of resilience autonomous system resilience through design and runtime measures"):
  with members:
    - "spherical text embedding"
    - "straggler mitigation with tiered gradient codes"
    - "certified adversarial robustness via randomized smoothing"
    - "lower bounds on adversarial robustness from optimal transport"
    - "a reinforcement learning based approach for joint multi agent decision making"
    - "vstore a data store for analytics on large videos"
    - "optimal server selection for straggler mitigation"
    - "formal security analysis of neural networks using symbolic intervals"

Connected through hyperedge HE-3 (induced by source paper "optimizing redundancy levels in master worker compute clusters for straggler mitigation"):
  with members:
    - "scheduling with predictions and the price of misprediction"
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "bigroots an effective approach for root cause analysis of stragglers in big data system"
    - "near optimal straggler mitigation for distributed gradient methods"
    - "a machine learning approach to routing"
    - "coded convolution for parallel and distributed computing within a deadline"
    - "short dot computing large linear transforms distributedly using coded short dot products"

Connected through hyperedge HE-4 (induced by source paper "straggler mitigation at scale"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "bigroots an effective approach for root cause analysis of stragglers in big data system"
    - "near optimal straggler mitigation for distributed gradient methods"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "gradient coding from cyclic mds codes and expander graphs"
    - "effective straggler mitigation which clones should attack and when"
    - "improving distributed gradient descent using reed solomon codes"

Connected through hyperedge HE-5 (induced by source paper "redundancy scheduling in systems with bi modal job service time distribution"):
  with members:
    - "straggler mitigation at scale"
    - "speeding up distributed machine learning using codes"
    - "on the effect of task to worker assignment in distributed computing systems with stragglers"
    - "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-6 (induced by source paper "optimal server selection for straggler mitigation"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "video streaming in distributed erasure coded storage systems stall duration analysis"
    - "joint latency and cost optimization for erasure coded data center storage"
    - "using straggler replication to reduce latency in large scale parallel computing extended version"
    - "when do redundant requests reduce latency"
    - "on the delay storage trade off in content download from coded distributed storage systems"

Connected through hyperedge HE-7 (induced by source paper "straggler mitigation with tiered gradient codes"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "efficient straggler replication in large scale parallel computing"

Connected through hyperedge HE-8 (induced by source paper "coded elastic computing"):
  with members:
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "hierarchical coded computation"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "straggler resilient and communication efficient distributed iterative linear solver"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "coded computing for distributed graph analytics"
    - "hierarchical coding for distributed computing"

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)