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: "gossip training for deep learning"
  Abstract: We address the issue of speeding up the training of convolutional networks. Here we study a distributed method adapted to stochastic gradient descent (SGD). The parallel optimization setup uses several threads, each applying individual gradient descents on a local variable. We propose a new way to share information between different threads inspired by gossip algorithms and showing good consensus convergence properties. Our method called GoSGD has the advantage to be fully asynchronous and decentralized. We compared our method to the recent EASGD in \cite{elastic} on CIFAR-10 show encouraging results.

Connected through hyperedge HE-1 (induced by source paper "edge intelligence paving the last mile of artificial intelligence with edge computing"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "peer to peer federated learning on graphs"
    - "pcamp performance comparison of machine learning packages on the edges"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "on device federated learning via blockchain and its latency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "communication compression for decentralized training"

Connected through hyperedge HE-2 (induced by source paper "edge intelligence the confluence of edge computing and artificial intelligence"):
  with members:
    - "convergence of edge computing and deep learning a comprehensive survey"
    - "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge"
    - "a graph neural network approach for scalable wireless power control"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "scan a scalable neural networks framework towards compact and efficient models"
    - "vehicular edge computing via deep reinforcement learning"
    - "openei an open framework for edge intelligence"
    - "uav communications for 5g and beyond recent advances and future trends"

Connected through hyperedge HE-3 (induced by source paper "decentralized federated learning a segmented gossip approach"):
  with members:
    - "towards federated learning at scale system design"
    - "federated learning strategies for improving communication efficiency"
    - "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed optimization beyond the datacenter"
    - "a reliable effective terascale linear learning system"

Connected through hyperedge HE-4 (induced by source paper "md gan multi discriminator generative adversarial networks for distributed datasets"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "generative adversarial parallelization"
    - "style transfer generative adversarial networks learning to play chess differently"
    - "multi generator generative adversarial nets"
    - "revisiting distributed synchronous sgd"
    - "distributed deep learning on edge devices feasibility via adaptive compression"
    - "invertible conditional gans for image editing"
    - "generative multi adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "distributed learning over unreliable networks"):
  with members:
    - "byzantine stochastic gradient descent"
    - "communication compression for decentralized training"
    - "sparcml high performance sparse communication for machine learning"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"
    - "cola decentralized linear learning"

Connected through hyperedge HE-6 (induced by source paper "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"):
  with members:
    - "imagenet training in minutes"
    - "scaling sgd batch size to 32k for imagenet training"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "how to scale distributed deep learning"
    - "distributed tensorflow with mpi"
    - "application of deep convolutional neural networks for detecting extreme weather in climate datasets"

Connected through hyperedge HE-7 (induced by source paper "stochastic gradient push for distributed deep learning"):
  with members:
    - "asynchronous subgradient push"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "scaling neural machine translation"
    - "exploring the limits of weakly supervised pretraining"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "attention is all you need"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "elastic gossip distributing neural network training using gossip like protocols"):
  with members:
    - "identity mappings in deep residual networks"
    - "how to scale distributed deep learning"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "firecaffe near linear acceleration of deep neural network training on compute clusters"
    - "staleness aware async sgd for distributed deep learning"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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.CV(Computer Vision and Pattern Recognition)