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: "efficient task replication for fast response times in parallel computation"
  Abstract: One typical use case of large-scale distributed computing in data centers is to decompose a computation job into many independent tasks and run them in parallel on different machines, sometimes known as the "embarrassingly parallel" computation. For this type of computation, one challenge is that the time to execute a task for each machine is inherently variable, and the overall response time is constrained by the execution time of the slowest machine. To address this issue, system designers introduce task replication, which sends the same task to multiple machines, and obtains result from the machine that finishes first. While task replication reduces response time, it usually increases resource usage. In this work, we propose a theoretical framework to analyze the trade-off between response time and resource usage. We show that, while in general, there is a tension between response time and resource usage, there exist scenarios where replicating tasks judiciously reduces completion time and resource usage simultaneously. Given the execution time distribution for machines, we investigate the conditions for a scheduling policy to achieve optimal performance trade-off, and propose efficient algorithms to search for optimal or near-optimal scheduling policies. Our analysis gives insights on when and why replication helps, which can be used to guide scheduler design in large-scale distributed computing systems.

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 "collage inference tolerating stragglers in distributed neural network inference using coding"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "yolov3 an incremental improvement"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dssd deconvolutional single shot detector"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "dsod learning deeply supervised object detectors from scratch"

Connected through hyperedge HE-3 (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-4 (induced by source paper "collage inference using coded redundancy for low variance distributed image classification"):
  with members:
    - "parity models a general framework for coding based resilience in ml inference"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "yolov3 an incremental improvement"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dsod learning deeply supervised object detectors from scratch"
    - "a fundamental tradeoff between computation and communication in distributed computing"

Connected through hyperedge HE-5 (induced by source paper "slack squeeze coded computing for adaptive straggler mitigation"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "parity models a general framework for coding based resilience in ml inference"
    - "hop heterogeneity aware decentralized training"
    - "collage inference tolerating stragglers in distributed neural network inference using coding"
    - "speeding up distributed machine learning using codes"
    - "oversketch approximate matrix multiplication for the cloud"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"

Connected through hyperedge HE-6 (induced by source paper "robust and communication efficient collaborative learning"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "an exact quantized decentralized gradient descent algorithm"
    - "speeding up distributed machine learning using codes"
    - "quantized decentralized consensus optimization"
    - "signsgd compressed optimisation for non convex problems"
    - "compressed distributed gradient descent communication efficient consensus over networks"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"

Connected through hyperedge HE-7 (induced by source paper "distributed matrix multiplication using speed adaptive coding"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "oversketch approximate matrix multiplication for the cloud"
    - "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"
    - "coding method for parallel iterative linear solver"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "a sequential approximation framework for coded distributed optimization"

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"
    - "using straggler replication to reduce latency in large scale parallel computing"
    - "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)