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: "experiments on parallel training of deep neural network using model averaging"
  Abstract: In this work we apply model averaging to parallel training of deep neural network (DNN). Parallelization is done in a model averaging manner. Data is partitioned and distributed to different nodes for local model updates, and model averaging across nodes is done every few minibatches. We use multiple GPUs for data parallelization, and Message Passing Interface (MPI) for communication between nodes, which allows us to perform model averaging frequently without losing much time on communication. We investigate the effectiveness of Natural Gradient Stochastic Gradient Descent (NG-SGD) and Restricted Boltzmann Machine (RBM) pretraining for parallel training in model-averaging framework, and explore the best setups in term of different learning rate schedules, averaging frequencies and minibatch sizes. It is shown that NG-SGD and RBM pretraining benefits parameter-averaging based model training. On the 300h Switchboard dataset, a 9.3 times speedup is achieved using 16 GPUs and 17 times speedup using 32 GPUs with limited decoding accuracy loss.

Connected through hyperedge HE-1 (induced by source paper "communication optimization strategies for distributed deep learning a survey"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "priority based parameter propagation for distributed dnn training"
    - "large batch training for lstm and beyond"
    - "scalable deep learning on distributed infrastructures challenges techniques and tools"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "redsync reducing synchronization bandwidth for distributed deep learning training system"

Connected through hyperedge HE-2 (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-3 (induced by source paper "local sgd with periodic averaging tighter analysis and adaptive synchronization"):
  with members:
    - "recycled admm improving the privacy and accuracy of distributed algorithms"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "signsgd compressed optimisation for non convex problems"

Connected through hyperedge HE-4 (induced by source paper "variance reduced local sgd with lower communication complexity"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "advances and open problems in federated learning"
    - "federated learning challenges methods and future directions"
    - "faster distributed deep net training computation and communication decoupled stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "federated learning strategies for improving communication efficiency"
    - "don t use large mini batches use local sgd"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"

Connected through hyperedge HE-5 (induced by source paper "on the convergence of local descent methods in federated learning"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "one shot federated learning"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"
    - "first analysis of local gd on heterogeneous data"
    - "local sgd with periodic averaging tighter analysis and adaptive synchronization"
    - "fair resource allocation in federated learning"
    - "efficient decentralized deep learning by dynamic model averaging"

Connected through hyperedge HE-6 (induced by source paper "split learning for collaborative deep learning in healthcare"):
  with members:
    - "expertmatcher automating ml model selection for clients using hidden representations"
    - "detailed comparison of communication efficiency of split learning and federated learning"
    - "distributed learning of deep neural network over multiple agents"
    - "no peek a survey of private distributed deep learning"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "revisiting distributed synchronous sgd"
    - "federated learning of deep networks using model averaging"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"):
  with members:
    - "don t use large mini batches use local sgd"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "parle parallelizing stochastic gradient descent"
    - "don t decay the learning rate increase the batch size"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "federated multi task learning"

Connected through hyperedge HE-8 (induced by source paper "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"):
  with members:
    - "don t use large mini batches use local sgd"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "signsgd compressed optimisation for non convex problems"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "gradient sparsification for communication efficient distributed optimization"
    - "parle parallelizing stochastic gradient descent"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "cocoa a general framework for communication efficient distributed optimization"

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)