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: "staleness aware async sgd for distributed deep learning"
  Abstract: Deep neural networks have been shown to achieve state-of-the-art performance in several machine learning tasks. Stochastic Gradient Descent (SGD) is the preferred optimization algorithm for training these networks and asynchronous SGD (ASGD) has been widely adopted for accelerating the training of large-scale deep networks in a distributed computing environment. However, in practice it is quite challenging to tune the training hyperparameters (such as learning rate) when using ASGD so as achieve convergence and linear speedup, since the stability of the optimization algorithm is strongly influenced by the asynchronous nature of parameter updates. In this paper, we propose a variant of the ASGD algorithm in which the learning rate is modulated according to the gradient staleness and provide theoretical guarantees for convergence of this algorithm. Experimental verification is performed on commonly-used image classification benchmarks: CIFAR10 and Imagenet to demonstrate the superior effectiveness of the proposed approach, compared to SSGD (Synchronous SGD) and the conventional ASGD algorithm.

Connected through hyperedge HE-1 (induced by source paper "learning home crowdsourced training of large neural networks using decentralized mixture of experts"):
  with members:
    - "scaling laws for neural language models"
    - "zero memory optimization towards training a trillion parameter models"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "natural compression for distributed deep learning"
    - "defending against neural fake news"
    - "large scale learning of general visual representations for transfer"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "pipemare asynchronous pipeline parallel dnn training"

Connected through hyperedge HE-2 (induced by source paper "elastic consistency a general consistency model for distributed stochastic gradient descent"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "communication compression for decentralized training"
    - "fault tolerance in iterative convergent machine learning"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "sparsified sgd with memory"
    - "toward understanding the impact of staleness in distributed machine learning"
    - "gradient sparsification for communication efficient distributed optimization"

Connected through hyperedge HE-3 (induced by source paper "secel privacy preserving verifiable and fault tolerant edge learning for autonomous vehicles"):
  with members:
    - "advances and open problems in federated learning"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "sdn based resource management for autonomous vehicular networks a multi access edge computing approach"
    - "federated learning for ultra reliable low latency v2v communications"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "how to backdoor federated learning"
    - "a survey on mobile edge computing the communication perspective"
    - "mobile edge computing a survey on architecture and computation offloading"

Connected through hyperedge HE-4 (induced by source paper "improving efficiency in large scale decentralized distributed training"):
  with members:
    - "distributed deep learning strategies for automatic speech recognition"
    - "lessons from building acoustic models with a million hours of speech"
    - "stochastic gradient push for distributed deep learning"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "dynamic mini batch sgd for elastic distributed training learning in the limbo of resources"):
  with members:
    - "bag of freebies for training object detection neural networks"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "an empirical model of large batch training"
    - "low latency video semantic segmentation"
    - "three factors influencing minima in sgd"
    - "slowfast networks for video recognition"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-6 (induced by source paper "taming unbalanced training workloads in deep learning with partial collective operations"):
  with members:
    - "priority based parameter propagation for distributed dnn training"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "sparcml high performance sparse communication for machine learning"
    - "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"
    - "stochastic gradient push for distributed deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "horovod fast and easy distributed deep learning in tensorflow"

Connected through hyperedge HE-7 (induced by source paper "making asynchronous stochastic gradient descent work for transformers"):
  with members:
    - "accelerating asynchronous stochastic gradient descent for neural machine translation"
    - "scaling neural machine translation"
    - "training tips for the transformer model"
    - "a call for clarity in reporting bleu scores"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "speeding up deep learning with transient servers"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "imagenet training in minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "performance modeling and evaluation of distributed deep learning frameworks on gpus"
    - "portfolio driven resource management for transient cloud servers"
    - "revisiting distributed synchronous sgd"

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.LG(Machine Learning)