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: "optimizing performance of recurrent neural networks on gpus"
  Abstract: As recurrent neural networks become larger and deeper, training times for single networks are rising into weeks or even months. As such there is a significant incentive to improve the performance and scalability of these networks. While GPUs have become the hardware of choice for training and deploying recurrent models, the implementations employed often make use of only basic optimizations for these architectures. In this article we demonstrate that by exposing parallelism between operations within the network, an order of magnitude speedup across a range of network sizes can be achieved over a naive implementation. We describe three stages of optimization that have been incorporated into the fifth release of NVIDIA's cuDNN: firstly optimizing a single cell, secondly a single layer, and thirdly the entire network.

Connected through hyperedge HE-1 (induced by source paper "all smiles variational autoencoder"):
  with members:
    - "conditional molecular design with deep generative models"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders"
    - "potentialnet for molecular property prediction"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"

Connected through hyperedge HE-2 (induced by source paper "a lightweight recurrent network for sequence modeling"):
  with members:
    - "rational recurrences"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "accelerating neural transformer via an average attention network"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "simple recurrent units for highly parallelizable recurrence"
    - "deriving neural architectures from sequence and graph kernels"
    - "recurrent additive networks"

Connected through hyperedge HE-3 (induced by source paper "lstm sharp an adaptable energy efficient hardware accelerator for long short term memory"):
  with members:
    - "learning to ask unanswerable questions for machine reading comprehension"
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "the emergence of number and syntax units in lstm language models"
    - "recent advances in recurrent neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "bidirectional attention flow for machine comprehension"
    - "eie efficient inference engine on compressed deep neural network"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-4 (induced by source paper "bppsa scaling back propagation by parallel scan algorithm"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "measuring the effects of data parallelism on neural network training"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "fma a dataset for music analysis"
    - "identity mappings in deep residual networks"
    - "compression of neural machine translation models via pruning"

Connected through hyperedge HE-5 (induced by source paper "neural arabic text diacritization state of the art results and a novel approach for machine translation"):
  with members:
    - "arabic text diacritization using deep neural networks"
    - "homograph disambiguation through selective diacritic restoration"
    - "block normalized gradient method an empirical study for training deep neural network"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "hybrid approaches for automatic vowelization of arabic texts"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "scheduling computation graphs of deep learning models on manycore cpus"):
  with members:
    - "factorization tricks for lstm networks"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "omnivore an optimizer for multi device deep learning on cpus and gpus"
    - "recurrent dropout without memory loss"
    - "wide residual networks"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-7 (induced by source paper "sparse persistent rnns squeezing large recurrent networks on chip"):
  with members:
    - "exploring sparsity in recurrent neural networks"
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "pruning filters for efficient convnets"
    - "compression of neural machine translation models via pruning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "a scalable near memory architecture for training deep neural networks on large in memory datasets"):
  with members:
    - "neurostream scalable and energy efficient deep learning with smart memory cubes"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "communication efficient learning of deep networks from decentralized data"
    - "deep image scaling up image recognition"
    - "deep residual learning for image recognition"
    - "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)