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: "learning to create and reuse words in open vocabulary neural language modeling"
  Abstract: Fixed-vocabulary language models fail to account for one of the most characteristic statistical facts of natural language: the frequent creation and reuse of new word types. Although character-level language models offer a partial solution in that they can create word types not attested in the training corpus, they do not capture the "bursty" distribution of such words. In this paper, we augment a hierarchical LSTM language model that generates sequences of word tokens character by character with a caching mechanism that learns to reuse previously generated words. To validate our model we construct a new open-vocabulary language modeling corpus (the Multilingual Wikipedia Corpus, MWC) from comparable Wikipedia articles in 7 typologically diverse languages and demonstrate the effectiveness of our model across this range of languages.

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"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "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"

Connected through hyperedge HE-2 (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"
    - "spell once summon anywhere a two level open vocabulary language model"

Connected through hyperedge HE-3 (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"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "character level models versus morphology in semantic role labeling"
    - "from characters to words to in between do we capture morphology"
    - "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-4 (induced by source paper "fast parametric learning with activation memorization"):
  with members:
    - "deep meta learning learning to learn in the concept space"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "learning to remember rare events"
    - "search engine guided non parametric neural machine translation"
    - "unbounded cache model for online language modeling with open vocabulary"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "pointing the unknown words"

Connected through hyperedge HE-5 (induced by source paper "learning to discover ground and use words with segmental neural language models"):
  with members:
    - "revisiting the hierarchical multiscale lstm"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "unsupervised neural word segmentation for chinese via segmental language modeling"
    - "on the state of the art of evaluation in neural language models"
    - "representations of language in a model of visually grounded speech signal"
    - "neural language modeling by jointly learning syntax and lexicon"
    - "from phonemes to images levels of representation in a recurrent neural model of visually grounded language learning"
    - "hierarchical multiscale recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "unsupervised word discovery with segmental neural language models"):
  with members:
    - "spell once summon anywhere a two level open vocabulary language model"
    - "unsupervised neural word segmentation for chinese via segmental language modeling"
    - "on the state of the art of evaluation in neural language models"
    - "representations of language in a model of visually grounded speech signal"
    - "neural language modeling by jointly learning syntax and lexicon"
    - "from phonemes to images levels of representation in a recurrent neural model of visually grounded language learning"
    - "hierarchical multiscale recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "spell once summon anywhere a two level open vocabulary language model"):
  with members:
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "mimicking word embeddings using subword rnns"
    - "regularizing and optimizing lstm language models"
    - "morphological priors for probabilistic neural word embeddings"
    - "character level language modeling with hierarchical recurrent neural networks"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "pointer sentinel mixture models"

Connected through hyperedge HE-8 (induced by source paper "are all languages equally hard to language model"):
  with members:
    - "unimorph 2 0 universal morphology"
    - "exploring the syntactic abilities of rnns with multi task learning"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "recurrent neural network regularization"

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)