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: "using straggler replication to reduce latency in large scale parallel computing"
  Abstract: Users expect fast and fluid response from today's cloud infrastructure. Large-scale computing frameworks such as MapReduce divide jobs into many parallel tasks and execute them on different machines to enable faster processing. But the tasks on the slowest machines (straggling tasks) become the bottleneck in the completion of the job. One way to combat the variability in machine response time, is to add replicas of straggling tasks and wait for one copy to finish. #R##N#In this paper we analyze how task replication strategies can be used to reduce latency, and their impact on the cost of computing resources. We use extreme value theory to show that the tail of the execution time distribution is the key factor in characterizing the trade-off between latency and computing cost. From this trade-off we can determine which task replication strategies reduce latency, without a large increase in computing cost. We also propose a heuristic algorithm to search for the best replication strategies when it is difficult to fit a simple distribution to model the empirical behavior of task execution time, and use the proposed analysis techniques. Evaluation of the heuristic policies on Google Trace data shows a significant latency reduction compared to the replication strategy used in MapReduce.

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 secure distributed matrix multiplication with flexible communication load"):
  with members:
    - "numerically stable polynomially coded computing"
    - "private sequential function computation"
    - "random khatri rao product codes for numerically stable distributed matrix multiplication"
    - "distributed and private coded matrix computation with flexible communication load"
    - "private information retrieval with private coded side information the multi server case"
    - "random convolutional coding for robust and straggler resilient distributed matrix computation"
    - "secure coded multi party computation for massive matrix operations"
    - "on the capacity of secure distributed matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "waiting but not aging age of information and utility optimization under the pull model"):
  with members:
    - "combinatorial sleeping bandits with fairness constraints"
    - "the age of information in networks moments distributions and sampling"
    - "minimizing content staleness in dynamo style replicated storage systems"
    - "optimizing information freshness in wireless networks under general interference constraints"
    - "optimizing age of information in wireless networks with perfect channel state information"
    - "status updates through multicast networks"
    - "the age of information in multihop networks"
    - "the power of waiting for more than one response in minimizing the age of information"

Connected through hyperedge HE-5 (induced by source paper "distributed and private coded matrix computation with flexible communication load"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "gasp codes for secure distributed matrix multiplication"
    - "rate efficiency and straggler robustness through partition in distributed two sided secure matrix computation"
    - "a unified coded deep neural network training strategy based on generalized polydot codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "on the capacity of secure distributed matrix multiplication"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "wireless map reduce distributed computing with full duplex radios and imperfect csi"

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"
    - "effective straggler mitigation which clones should attack and when"

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 "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"
    - "delay comparison of delivery and coding policies in data clusters"
    - "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"
    - "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"

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.DC(Distributed, Parallel, and Cluster Computing)