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: "scaling recurrent neural network language models"
  Abstract: This paper investigates the scaling properties of Recurrent Neural Network Language Models (RNNLMs). We discuss how to train very large RNNs on GPUs and address the questions of how RNNLMs scale with respect to model size, training-set size, computational costs and memory. Our analysis shows that despite being more costly to train, RNNLMs obtain much lower perplexities on standard benchmarks than n-gram models. We train the largest known RNNs and present relative word error rates gains of 18% on an ASR task. We also present the new lowest perplexities on the recently released billion word language modelling benchmark, 1 BLEU point gain on machine translation and a 17% relative hit rate gain in word prediction.

Connected through hyperedge HE-1 (induced by source paper "accelerating training of deep neural networks with a standardization loss"):
  with members:
    - "group normalization"
    - "understanding batch normalization"
    - "decorrelated batch normalization"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "progressive growing of gans for improved quality stability and variation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "comparison of batch normalization and weight normalization algorithms for the large scale image classification"

Connected through hyperedge HE-2 (induced by source paper "kite automatic speech recognition for unmanned aerial vehicles"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "vision meets drones a challenge"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "using the output embedding to improve language models"
    - "musan a music speech and noise corpus"

Connected through hyperedge HE-3 (induced by source paper "deep enhanced representation for implicit discourse relation recognition"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a recurrent neural model with attention for the recognition of chinese implicit discourse relations"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "a structured self attentive sentence embedding"
    - "language modeling with gated convolutional networks"
    - "neural word segmentation learning for chinese"

Connected through hyperedge HE-4 (induced by source paper "discriminative training of rnnlms with the average word error criterion"):
  with members:
    - "large margin neural language model"
    - "optimizing expected word error rate via sampling for speech recognition"
    - "contrastive entropy a new evaluation metric for unnormalized language models"

Connected through hyperedge HE-5 (induced by source paper "recurrent segmentation for variable computational budgets"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "stacked deconvolutional network for semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "bridging category level and instance level semantic image segmentation"

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 "a simple language model based on pmi matrix approximations"):
  with members:
    - "exploring the limits of language modeling"
    - "neural machine translation of rare words with subword units"
    - "strategies for training large vocabulary neural language models"
    - "blackout speeding up recurrent neural network language models with very large vocabularies"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"
    - "notes on noise contrastive estimation and negative sampling"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-8 (induced by source paper "exploring the limits of language modeling"):
  with members:
    - "multilingual language processing from bytes"
    - "a neural attention model for abstractive sentence summarization"
    - "document context language models"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "unsupervised learning of video representations using lstms"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "character aware neural language models"
    - "larger context language modelling"

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.CL(Computation and Language)