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: "omnivore an optimizer for multi device deep learning on cpus and gpus"
  Abstract: We study the factors affecting training time in multi-device deep learning systems. Given a specification of a convolutional neural network, our goal is to minimize the time to train this model on a cluster of commodity CPUs and GPUs. We first focus on the single-node setting and show that by using standard batching and data-parallel techniques, throughput can be improved by at least 5.5x over state-of-the-art systems on CPUs. This ensures an end-to-end training speed directly proportional to the throughput of a device regardless of its underlying hardware, allowing each node in the cluster to be treated as a black box. Our second contribution is a theoretical and empirical study of the tradeoffs affecting end-to-end training time in a multiple-device setting. We identify the degree of asynchronous parallelization as a key factor affecting both hardware and statistical efficiency. We see that asynchrony can be viewed as introducing a momentum term. Our results imply that tuning momentum is critical in asynchronous parallel configurations, and suggest that published results that have not been fully tuned might report suboptimal performance for some configurations. For our third contribution, we use our novel understanding of the interaction between system and optimization dynamics to provide an efficient hyperparameter optimizer. Our optimizer involves a predictive model for the total time to convergence and selects an allocation of resources to minimize that time. We demonstrate that the most popular distributed deep learning systems fall within our tradeoff space, but do not optimize within the space. By doing this optimization, our prototype runs 1.9x to 12x faster than the fastest state-of-the-art systems.

Connected through hyperedge HE-1 (induced by source paper "distributed learning of deep neural networks using independent subnet training"):
  with members:
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "mlsys the new frontier of machine learning systems"
    - "large batch training for lstm and beyond"
    - "communication efficient distributed sgd with sketching"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "inefficiency of k fac for large batch size training"
    - "survey of dropout methods for deep neural networks"
    - "on the ineffectiveness of variance reduced optimization for deep learning"

Connected through hyperedge HE-2 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "verdictdb universalizing approximate query processing"
    - "measuring the effects of data parallelism on neural network training"
    - "hemingway modeling distributed optimization algorithms"
    - "unbiased estimates for linear regression via volume sampling"
    - "database learning toward a database that becomes smarter every time"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-3 (induced by source paper "dynamic stale synchronous parallel distributed training for deep learning"):
  with members:
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "revisiting distributed synchronous sgd"
    - "mllib machine learning in apache spark"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "one weird trick for parallelizing convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "heterogeneity aware gradient coding for straggler tolerance"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "polynomially coded regression optimal straggler mitigation via data encoding"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "gradient coding from cyclic mds codes and expander graphs"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-5 (induced by source paper "distributed machine learning through heterogeneous edge systems"):
  with members:
    - "wireless network intelligence at the edge"
    - "federated learning strategies for improving communication efficiency"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-6 (induced by source paper "panda facilitating usable ai development"):
  with members:
    - "rafiki machine learning as an analytics service system"
    - "medical concept embedding with time aware attention"
    - "deep forest"
    - "forkbase an efficient storage engine for blockchain and forkable applications"
    - "progressive neural architecture search"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "the case for learned index structures"
    - "thoracic disease identification and localization with limited supervision"

Connected through hyperedge HE-7 (induced by source paper "scheduling computation graphs of deep learning models on manycore cpus"):
  with members:
    - "factorization tricks for lstm networks"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "recurrent dropout without memory loss"
    - "optimizing performance of recurrent neural networks on gpus"
    - "wide residual networks"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "accelerating asynchronous stochastic gradient descent for neural machine translation"):
  with members:
    - "marian fast neural machine translation in c"
    - "don t use large mini batches use local sgd"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "multi representation ensembles and delayed sgd updates improve syntax based nmt"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "sparse communication for distributed gradient descent"
    - "edinburgh neural machine translation systems for wmt 16"

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)