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: "blackout speeding up recurrent neural network language models with very large vocabularies"
  Abstract: We propose BlackOut, an approximation algorithm to efficiently train massive recurrent neural network language models (RNNLMs) with million word vocabularies. BlackOut is motivated by using a discriminative loss, and we describe a new sampling strategy which significantly reduces computation while improving stability, sample efficiency, and rate of convergence. One way to understand BlackOut is to view it as an extension of the DropOut strategy to the output layer, wherein we use a discriminative training loss and a weighted sampling scheme. We also establish close connections between BlackOut, importance sampling, and noise contrastive estimation (NCE). Our experiments, on the recently released one billion word language modeling benchmark, demonstrate scalability and accuracy of BlackOut; we outperform the state-of-the art, and achieve the lowest perplexity scores on this dataset. Moreover, unlike other established methods which typically require GPUs or CPU clusters, we show that a carefully implemented version of BlackOut requires only 1-10 days on a single machine to train a RNNLM with a million word vocabulary and billions of parameters on one billion words. Although we describe BlackOut in the context of RNNLM training, it can be used to any networks with large softmax output layers.

Connected through hyperedge HE-1 (induced by source paper "hide and tell learning to bridge photo streams for visual storytelling"):
  with members:
    - "hierarchical photo scene encoder for album storytelling"
    - "linknet relational embedding for scene graph"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "learning by asking questions"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-2 (induced by source paper "accelerated reinforcement learning for sentence generation by vocabulary prediction"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "learning to parse and translate improves neural machine translation"
    - "neural machine translation with source side latent graph parsing"
    - "neural machine translation with word predictions"
    - "improving neural machine translation with conditional sequence generative adversarial nets"
    - "neural machine translation via binary code prediction"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "von mises fisher loss for training sequence to sequence models with continuous outputs"):
  with members:
    - "billion scale similarity search with gpus"
    - "mimicking word embeddings using subword rnns"
    - "stronger baselines for trustable results in neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "neural machine translation via binary code prediction"
    - "enriching word vectors with subword information"
    - "using the output embedding to improve language models"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-4 (induced by source paper "recurrent neural networks with top k gains for session based recommendations"):
  with members:
    - "recurrent latent variable networks for session based recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "theano a python framework for fast computation of mathematical expressions"
    - "collaborative filtering with recurrent neural networks"
    - "session based recommendations with recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "fast als based tensor factorization for context aware recommendation from implicit feedback"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-5 (induced by source paper "self normalization properties of language modeling"):
  with members:
    - "a batch noise contrastive estimation approach for training large vocabulary language models"
    - "a simple language model based on pmi matrix approximations"
    - "exploring the limits of language modeling"
    - "pointer sentinel mixture models"
    - "strategies for training large vocabulary neural language models"
    - "recurrent neural network regularization"
    - "a fast and simple algorithm for training neural probabilistic language models"

Connected through hyperedge HE-6 (induced by source paper "frustratingly short attention spans in neural language modeling"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "long short term memory networks for machine reading"
    - "higher order recurrent neural networks"
    - "text understanding with the attention sum reader network"
    - "consensus attention based neural networks for chinese reading comprehension"
    - "key value memory networks for directly reading documents"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "reference aware language models"

Connected through hyperedge HE-7 (induced by source paper "neural machine translation with source side latent graph parsing"):
  with members:
    - "towards bidirectional hierarchical representations for attention based neural machine translation"
    - "learning to parse and translate improves neural machine translation"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "tree to sequence attentional neural machine translation"
    - "enriching word vectors with subword information"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "dependency parsing as head selection"

Connected through hyperedge HE-8 (induced by source paper "slim embedding layers for recurrent neural language models"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "efficient softmax approximation for gpus"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "language modeling with gated convolutional networks"
    - "compressing neural language models by sparse word representations"
    - "recurrent highway networks"
    - "lightrnn memory and computation efficient recurrent neural networks"
    - "using the output embedding to improve language models"

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)