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: "spell once summon anywhere a two level open vocabulary language model"
  Abstract: We show how to deploy recurrent neural networks within a hierarchical Bayesian language model. Our generative story combines a standard RNN language model (generating the word tokens in each sentence) with an RNN-based spelling model (generating the letters in each word type). These two RNNs respectively capture sentence structure and word structure, and are kept separate as in linguistics. The model can generate spellings for novel words in context and thus serves as an open-vocabulary language model. For known words, embeddings are naturally inferred by combining evidence from type spelling and token context. We compare to a number of baselines and previous work, establishing state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "maybe deep neural networks are the best choice for modeling source code"):
  with members:
    - "code completion with neural attention and pointer networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "attention is all you need"
    - "context2name a deep learning based approach to infer natural variable names from usage contexts"
    - "a survey of machine learning for big code and naturalness"

Connected through hyperedge HE-2 (induced by source paper "an embarrassingly simple approach for transfer learning from pretrained language models"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "ntua slp at iest 2018 ensemble of neural transfer methods for implicit emotion classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"

Connected through hyperedge HE-3 (induced by source paper "multifit efficient multi lingual language model fine tuning"):
  with members:
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual language model pretraining"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "are all languages equally hard to language model"

Connected through hyperedge HE-4 (induced by source paper "counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology"):
  with members:
    - "gender bias in contextualized word embeddings"
    - "gender bias in coreference resolution"
    - "gender bias in neural natural language processing"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "biased embeddings from wild data measuring understanding and removing"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "hard non monotonic attention for character level transduction"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"

Connected through hyperedge HE-5 (induced by source paper "neural language modeling with visual features"):
  with members:
    - "visually grounded situated learning in neural models"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "towards automatic learning of procedures from web instructional videos"
    - "localizing moments in video with natural language"
    - "the something something video database for learning and evaluating visual common sense"
    - "grounding of textual phrases in images by reconstruction"
    - "youtube 8m a large scale video classification benchmark"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"

Connected through hyperedge HE-6 (induced by source paper "what kind of language is hard to language model"):
  with members:
    - "unimorph 2 0 universal morphology"
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "an analysis of neural language modeling at multiple scales"
    - "are all languages equally hard to language model"
    - "unsupervised identification of translationese"
    - "neural machine translation of rare words with subword units"
    - "a parallel corpus of translationese"

Connected through hyperedge HE-7 (induced by source paper "better character language modeling through morphology"):
  with members:
    - "what do character level models learn about morphology the case of dependency parsing"
    - "character level models versus morphology in semantic role labeling"
    - "from characters to words to in between do we capture morphology"
    - "learning to create and reuse words in open vocabulary neural language modeling"
    - "what do neural machine translation models learn about morphology"
    - "adam a method for stochastic optimization"
    - "compositional morphology for word representations and language modelling"

Connected through hyperedge HE-8 (induced by source paper "adaptive input representations for neural language modeling"):
  with members:
    - "neural lattice language models"
    - "scaling neural machine translation"
    - "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"
    - "language modeling with gated convolutional networks"

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)