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: "exploring hidden dimensions in parallelizing convolutional neural networks"
  Abstract: The past few years have witnessed growth in the size and computational requirements for training deep convolutional neural networks. Current approaches parallelize the training process onto multiple devices by applying a single parallelization strategy (e.g., data or model parallelism) to all layers in a network. Although easy to reason about, this design results in suboptimal runtime performance in large-scale distributed training, since different layers in a network may prefer different parallelization strategies. In this paper, we propose layer-wise parallelism that allows each layer in a network to use an individual parallelization strategy. We jointly optimize how each layer is parallelized by solving a graph search problem. Our experiments show that layer-wise parallelism outperforms current parallelization approaches by increasing training speed, reducing communication costs, achieving better scalability to multiple GPUs, while maintaining the same network accuracy.

Connected through hyperedge HE-1 (induced by source paper "ordering chaos memory aware scheduling of irregularly wired neural networks for edge devices"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "automatic model selection for neural networks"
    - "swiftnet using graph propagation as meta knowledge to search highly representative neural architectures"
    - "discovering neural wirings"
    - "pytorch an imperative style high performance deep learning library"
    - "learned step size quantization"
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-2 (induced by source paper "simulating performance of ml systems with offline profiling"):
  with members:
    - "beyond data and model parallelism for deep neural networks"
    - "device placement optimization with reinforcement learning"
    - "densely connected convolutional networks"
    - "tensorflow a system for large scale machine learning"
    - "rethinking the inception architecture for computer vision"
    - "one weird trick for parallelizing convolutional neural networks"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-3 (induced by source paper "accelerated training for cnn distributed deep learning through automatic resource aware layer placement"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "beyond data and model parallelism for deep neural networks"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "gradient sparsification for communication efficient distributed optimization"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"

Connected through hyperedge HE-4 (induced by source paper "optimizing multi gpu parallelization strategies for deep learning training"):
  with members:
    - "crossbow scaling deep learning with small batch sizes on multi gpu servers"
    - "findings of the 2016 wmt shared task on cross lingual pronoun prediction"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "communication scheduling as a first class citizen in distributed machine learning systems"
    - "scaling neural machine translation"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "three factors influencing minima in sgd"
    - "on scale out deep learning training for cloud and hpc"

Connected through hyperedge HE-5 (induced by source paper "supporting very large models using automatic dataflow graph partitioning"):
  with members:
    - "beyond data and model parallelism for deep neural networks"
    - "profile guided memory optimization for deep neural networks"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "device placement optimization with reinforcement learning"
    - "a systematic approach to blocking convolutional neural networks"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "wide residual networks"

Connected through hyperedge HE-6 (induced by source paper "distributed equivalent substitution training for large scale recommender systems"):
  with members:
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "integrated model batch and domain parallelism in training neural networks"
    - "device placement optimization with reinforcement learning"
    - "revisiting distributed synchronous sgd"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-7 (induced by source paper "efficient and robust parallel dnn training through model parallelism on multi gpu platform"):
  with members:
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "beyond data and model parallelism for deep neural networks"
    - "regularized evolution for image classifier architecture search"
    - "mxnet mpi embedding mpi parallelism in parameter server task model for scaling deep learning"
    - "training neural networks using features replay"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "self normalizing neural networks"

Connected through hyperedge HE-8 (induced by source paper "beyond data and model parallelism for deep neural networks"):
  with members:
    - "device placement optimization with reinforcement learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "densely connected convolutional networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "rethinking the inception architecture for computer vision"

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)