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: "poseidon a system architecture for efficient gpu based deep learning on multiple machines"
  Abstract: Deep learning (DL) has achieved notable successes in many machine learning tasks. A number of frameworks have been developed to expedite the process of designing and training deep neural networks (DNNs), such as Caffe, Torch and Theano. Currently they can harness multiple GPUs on a single machine, but are unable to use GPUs that are distributed across multiple machines; as even average-sized DNNs can take days to train on a single GPU with 100s of GBs to TBs of data, distributed GPUs present a prime opportunity for scaling up DL. However, the limited bandwidth available on commodity Ethernet networks presents a bottleneck to distributed GPU training, and prevents its trivial realization. #R##N#To investigate how to adapt existing frameworks to efficiently support distributed GPUs, we propose Poseidon, a scalable system architecture for distributed inter-machine communication in existing DL frameworks. We integrate Poseidon with Caffe and evaluate its performance at training DNNs for object recognition. Poseidon features three key contributions that accelerate DNN training on clusters: (1) a three-level hybrid architecture that allows Poseidon to support both CPU-only and GPU-equipped clusters, (2) a distributed wait-free backpropagation (DWBP) algorithm to improve GPU utilization and to balance communication, and (3) a structure-aware communication protocol (SACP) to minimize communication overheads. We empirically show that Poseidon converges to same objectives as a single machine, and achieves state-of-art training speedup across multiple models and well-established datasets using a commodity GPU cluster of 8 nodes (e.g. 4.5x speedup on AlexNet, 4x on GoogLeNet, 4x on CIFAR-10). On the much larger ImageNet22K dataset, Poseidon with 8 nodes achieves better speedup and competitive accuracy to recent CPU-based distributed systems such as Adam and Le et al., which use 10s to 1000s of nodes.

Connected through hyperedge HE-1 (induced by source paper "declarative recursive computation on an rdbms or why you should use a database for distributed machine learning"):
  with members:
    - "ampnet asynchronous model parallel training for dynamic neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "an overview of gradient descent optimization algorithms"
    - "tensorflow a system for large scale machine learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-2 (induced by source paper "stanza layer separation for distributed training in deep learning"):
  with members:
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "performance modeling and evaluation of distributed deep learning frameworks on gpus"
    - "device placement optimization with reinforcement learning"
    - "sparse communication for distributed gradient descent"
    - "revisiting distributed synchronous sgd"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"

Connected through hyperedge HE-3 (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"
    - "gossip training for deep learning"
    - "how to scale distributed deep learning"
    - "distributed tensorflow with mpi"

Connected through hyperedge HE-4 (induced by source paper "mltuner system support for automatic machine learning tuning"):
  with members:
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-5 (induced by source paper "cavs a vertex centric programming interface for dynamic neural networks"):
  with members:
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "deep learning with dynamic computation graphs"
    - "on the fly operation batching in dynamic computation graphs"
    - "semantic object parsing with graph lstm"
    - "efficient softmax approximation for gpus"
    - "combining the best of convolutional layers and recurrent layers a hybrid network for semantic segmentation"
    - "tensorflow a system for large scale machine learning"
    - "transition based dependency parsing with stack long short term memory"

Connected through hyperedge HE-6 (induced by source paper "structured generative adversarial networks"):
  with members:
    - "toward controlled generation of text"
    - "triple generative adversarial nets"
    - "improved training of wasserstein gans"
    - "zm net real time zero shot image manipulation network"
    - "detach and adapt learning cross domain disentangled deep representation"
    - "recurrent topic transition gan for visual paragraph generation"
    - "learning what and where to draw"
    - "temporal ensembling for semi supervised learning"

Connected through hyperedge HE-7 (induced by source paper "distributed training large scale deep architectures"):
  with members:
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "comparative study of deep learning software frameworks"
    - "benchmarking state of the art deep learning software tools"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "caffe con troll shallow ideas to speed up deep learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-8 (induced by source paper "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "recurrent topic transition gan for visual paragraph generation"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "firecaffe near linear acceleration of deep neural network training on compute clusters"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"

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)