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: "how to scale distributed deep learning"
  Abstract: Training time on large datasets for deep neural networks is the principal workflow bottleneck in a number of important applications of deep learning, such as object classification and detection in automatic driver assistance systems (ADAS). To minimize training time, the training of a deep neural network must be scaled beyond a single machine to as many machines as possible by distributing the optimization method used for training. While a number of approaches have been proposed for distributed stochastic gradient descent (SGD), at the current time synchronous approaches to distributed SGD appear to be showing the greatest performance at large scale. Synchronous scaling of SGD suffers from the need to synchronize all processors on each gradient step and is not resilient in the face of failing or lagging processors. In asynchronous approaches using parameter servers, training is slowed by contention to the parameter server. In this paper we compare the convergence of synchronous and asynchronous SGD for training a modern ResNet network architecture on the ImageNet classification problem. We also propose an asynchronous method, gossiping SGD, that aims to retain the positive features of both systems by replacing the all-reduce collective operation of synchronous training with a gossip aggregation algorithm. We find, perhaps counterintuitively, that asynchronous SGD, including both elastic averaging and gossiping, converges faster at fewer nodes (up to about 32 nodes), whereas synchronous SGD scales better to more nodes (up to about 100 nodes).

Connected through hyperedge HE-1 (induced by source paper "the future of digital health with federated learning"):
  with members:
    - "multi site fmri analysis using privacy preserving federated learning and domain adaptation abide results"
    - "overcoming forgetting in federated learning on non iid data"
    - "advances and open problems in federated learning"
    - "deep leakage from gradients"
    - "privacy preserving federated brain tumour segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "peer to peer federated learning on graphs"
    - "p3sgd patient privacy preserving sgd for regularizing deep cnns in pathological image classification"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "doublesqueeze parallel stochastic gradient descent with double pass error compensated compression"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "communication compression for decentralized training"
    - "sparcml high performance sparse communication for machine learning"
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"

Connected through hyperedge HE-5 (induced by source paper "a random gossip bmuf process for neural language modeling"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "attention is all you need"
    - "empirical evaluation of parallel training algorithms on acoustic modeling"
    - "efficient softmax approximation for gpus"
    - "exploring the limits of language modeling"

Connected through hyperedge HE-6 (induced by source paper "bandwidth reduction using importance weighted pruning on ring allreduce"):
  with members:
    - "scaling sgd batch size to 32k for imagenet training"
    - "revisiting distributed synchronous sgd"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "distributed byzantine tolerant stochastic gradient descent in the era of big data"):
  with members:
    - "byzantine stochastic gradient descent"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"

Connected through hyperedge HE-8 (induced by source paper "on the computational inefficiency of large batch sizes for stochastic gradient descent"):
  with members:
    - "large scale language modeling converging on 40gb of text in four hours"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "three factors influencing minima in sgd"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "imagenet training in minutes"
    - "scaling sgd batch size to 32k for imagenet training"
    - "don t decay the learning rate increase the batch size"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"

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)