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: "pragmatic neural language modelling in machine translation"
  Abstract: This paper presents an in-depth investigation on integrating neural language models in translation systems. Scaling neural language models is a difficult task, but crucial for real-world applications. This paper evaluates the impact on end-to-end MT quality of both new and existing scaling techniques. We show when explicitly normalising neural models is necessary and what optimisation tricks one should use in such scenarios. We also focus on scalable training algorithms and investigate noise contrastive estimation and diagonal contexts as sources for further speed improvements. We explore the trade-offs between neural models and back-off n-gram models and find that neural models make strong candidates for natural language applications in memory constrained environments, yet still lag behind traditional models in raw translation quality. We conclude with a set of recommendations one should follow to build a scalable neural language model for MT.

Connected through hyperedge HE-1 (induced by source paper "improving word representations a sub sampled unigram distribution for negative sampling"):
  with members:
    - "enriching word vectors with subword information"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "strategies for training large vocabulary neural language models"
    - "learning word representations with hierarchical sparse coding"
    - "sequence to sequence learning with neural networks"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "a fast and simple algorithm for training neural probabilistic language models"

Connected through hyperedge HE-2 (induced by source paper "adaptive input representations for neural language modeling"):
  with members:
    - "neural lattice language models"
    - "scaling neural machine translation"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "character level language modeling with deeper self attention"
    - "attention is all you need"
    - "sgdr stochastic gradient descent with restarts"
    - "efficient softmax approximation for gpus"
    - "tying word vectors and word classifiers a loss framework for language modeling"

Connected through hyperedge HE-3 (induced by source paper "attention based vocabulary selection for nmt decoding"):
  with members:
    - "vocabulary selection strategies for neural machine translation"
    - "vocabulary manipulation for neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "on using very large target vocabulary for neural machine translation"
    - "sequence to sequence learning with neural networks"
    - "addressing the rare word problem in neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "improving language modelling with noise contrastive estimation"):
  with members:
    - "strategies for training large vocabulary neural language models"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-5 (induced by source paper "scalable machine translation in memory constrained environments"):
  with members:
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "compressing neural networks with the hashing trick"
    - "compositional morphology for word representations and language modelling"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "one billion word benchmark for measuring progress in statistical language modeling"
    - "a fast and simple algorithm for training neural probabilistic language models"

Connected through hyperedge HE-6 (induced by source paper "transfer learning for low resource neural machine translation"):
  with members:
    - "neural machine translation"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "transfer learning for speech and language processing"
    - "effective approaches to attention based neural machine translation"
    - "scaling recurrent neural network language models"
    - "neural machine translation by jointly learning to align and translate"
    - "recurrent neural network regularization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (induced by source paper "generalizing and hybridizing count based and neural language models"):
  with members:
    - "scaling recurrent neural network language models"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "a diversity promoting objective function for neural conversation models"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "natural language understanding with distributed representation"):
  with members:
    - "lstm a search space odyssey"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "character based neural machine translation"
    - "document context language models"
    - "deep learning in neural networks"
    - "effective approaches to attention based neural machine translation"
    - "vqa visual question answering"
    - "character aware neural 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.CL(Computation and Language)