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: "achieving open vocabulary neural machine translation with hybrid word character models"
  Abstract: Nearly all previous work on neural machine translation (NMT) has used quite restricted vocabularies, perhaps with a subsequent method to patch in unknown words. This paper presents a novel word-character solution to achieving open vocabulary NMT. We build hybrid systems that translate mostly at the word level and consult the character components for rare words. Our character-level recurrent neural networks compute source word representations and recover unknown target words when needed. The twofold advantage of such a hybrid approach is that it is much faster and easier to train than character-based ones; at the same time, it never produces unknown words as in the case of word-based models. On the WMT'15 English to Czech translation task, this hybrid approach offers an addition boost of +2.1-11.4 BLEU points over models that already handle unknown words. Our best system achieves a new state-of-the-art result with 20.7 BLEU score. We demonstrate that our character models can successfully learn to not only generate well-formed words for Czech, a highly-inflected language with a very complex vocabulary, but also build correct representations for English source words.

Connected through hyperedge HE-1 (induced by source paper "authorship attribution in bangla literature using character level cnn"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "a joint model for word embedding and word morphology"
    - "charagram embedding words and sentences via character n grams"
    - "character level and multi channel convolutional neural networks for large scale authorship attribution"
    - "exploring the limits of language modeling"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "character level convolutional networks for text classification"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-2 (induced by source paper "on the linguistic representational power of neural machine translation models"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "linguistic knowledge and transferability of contextual representations"
    - "the missing ingredient in zero shot neural machine translation"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "bpe and charcnns for translation of morphology a cross lingual comparison and analysis"

Connected through hyperedge HE-3 (induced by source paper "glyce glyph vectors for chinese character representations"):
  with members:
    - "toward fast and accurate neural chinese word segmentation with multi criteria learning"
    - "chinese ner using lattice lstm"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "deep contextualized word representations"
    - "state of the art chinese word segmentation with bi lstms"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "improving pre trained multilingual models with vocabulary expansion"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "gromov wasserstein alignment of word embedding spaces"
    - "generating wikipedia by summarizing long sequences"
    - "bilingual expert can find translation errors"
    - "universal neural machine translation for extremely low resource languages"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "on the limitations of unsupervised bilingual dictionary induction"

Connected through hyperedge HE-5 (induced by source paper "gating mechanisms for combining character and word level word representations an empirical study"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "learning universal sentence representations with mean max attention autoencoder"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-6 (induced by source paper "subword language model for query auto completion"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "personalized neural language models for real world query auto completion"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "learning to discover ground and use words with segmental neural language models"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "personalized language model for query auto completion"
    - "distilling knowledge for search based structured prediction"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"

Connected through hyperedge HE-7 (induced by source paper "augmenting neural machine translation with knowledge graphs"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "learning beyond datasets knowledge graph augmented neural networks for natural language processing"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "machine translation using semantic web technologies a survey"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "learning word vectors for 157 languages"
    - "the importance of being recurrent for modeling hierarchical structure"

Connected through hyperedge HE-8 (induced by source paper "generating black box adversarial examples for text classifiers using a deep reinforced model"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "spatially transformed adversarial examples"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "generating natural language adversarial examples"
    - "gray box adversarial training"
    - "adversarial texts with gradient methods"
    - "motivating the rules of the game for adversarial example research"

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)