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: "effective straggler mitigation which clones should attack and when"
  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 and allows for greater achievable latency and cost tradeoff region compared to 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.

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 "stochastic gradient coding for straggler mitigation in distributed learning"):
  with members:
    - "fundamental limits of approximate gradient coding"
    - "analyzing the download time of availability codes"
    - "distributed stochastic gradient descent using ldgm codes"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "coded cooperative computation for internet of things"
    - "speeding up distributed machine learning using codes"
    - "anytime stochastic gradient descent a time to hear from all the workers"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"

Connected through hyperedge HE-3 (induced by source paper "private and rateless adaptive coded matrix vector multiplication"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "analyzing the download time of availability codes"
    - "secure coded cooperative computation at the heterogeneous edge against byzantine attacks"
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "speeding up distributed machine learning using codes"
    - "anytime stochastic gradient descent a time to hear from all the workers"
    - "gasp codes for secure distributed matrix multiplication"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"

Connected through hyperedge HE-4 (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"
    - "straggler mitigation by delayed relaunch of tasks"
    - "near optimal straggler mitigation for distributed gradient methods"
    - "a machine learning approach to routing"
    - "coded convolution for parallel and distributed computing within a deadline"

Connected through hyperedge HE-5 (induced by source paper "secure coded cooperative computation at the heterogeneous edge against byzantine attacks"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coded convolution for parallel and distributed computing within a deadline"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "minimizing latency for secure distributed computing"
    - "improving distributed gradient descent using reed solomon codes"

Connected through hyperedge HE-6 (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"
    - "straggler mitigation by delayed relaunch of tasks"
    - "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"
    - "improving distributed gradient descent using reed solomon codes"

Connected through hyperedge HE-7 (induced by source paper "gradient coding based on block designs for mitigating adversarial stragglers"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "oversketch approximate matrix multiplication for the cloud"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "near optimal straggler mitigation for distributed gradient methods"
    - "occupy the cloud distributed computing for the 99"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "gradient coding from cyclic mds codes and expander graphs"
    - "revisiting distributed synchronous sgd"

Connected through hyperedge HE-8 (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"
    - "straggler mitigation by delayed relaunch of tasks"
    - "efficient redundancy techniques for latency reduction in cloud systems"
    - "on the delay storage trade off in content download from coded distributed storage systems"

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)