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: "a universal part of speech tagset"
  Abstract: To facilitate future research in unsupervised induction of syntactic structure and to standardize best-practices, we propose a tagset that consists of twelve universal part-of-speech categories. In addition to the tagset, we develop a mapping from 25 different treebank tagsets to this universal set. As a result, when combined with the original treebank data, this universal tagset and mapping produce a dataset consisting of common parts-of-speech for 22 different languages. We highlight the use of this resource via two experiments, including one that reports competitive accuracies for unsupervised grammar induction without gold standard part-of-speech tags.

Connected through hyperedge HE-1 (induced by source paper "multilingual alignment of contextual word representations"):
  with members:
    - "cross lingual bert transformation for zero shot dependency parsing"
    - "a survey of cross lingual word embedding models"
    - "how multilingual is multilingual bert"
    - "context aware cross lingual mapping"
    - "simple and effective paraphrastic similarity from parallel translations"
    - "cross lingual language model pretraining"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "xnli evaluating cross lingual sentence representations"

Connected through hyperedge HE-2 (induced by source paper "parsing thai social data a new challenge for thai nlp"):
  with members:
    - "a simple lstm model for transition based dependency parsing"
    - "semantic tagging with deep residual networks"
    - "deep biaffine attention for neural dependency parsing"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "transition based dependency parsing with stack long short term memory"

Connected through hyperedge HE-3 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "deep learning for sentiment analysis a survey"
    - "code2vec learning distributed representations of code"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "morphosyntactic tagging with a meta bilstm model over context sensitive token encodings"
    - "generalizing word embeddings using bag of subwords"
    - "dynamic meta embeddings for improved sentence representations"

Connected through hyperedge HE-4 (induced by source paper "embedding projection for targeted cross lingual sentiment model comparisons and a real world study"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "unsupervised statistical machine translation"
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "multibooked a corpus of basque and catalan hotel reviews annotated for aspect level sentiment classification"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "are all languages equally hard to language model"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-5 (induced by source paper "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"):
  with members:
    - "context aware cross lingual mapping"
    - "linguistic knowledge and transferability of contextual representations"
    - "gromov wasserstein alignment of word embedding spaces"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "parameter sharing between dependency parsers for related languages"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "a comparison of architectures and pretraining methods for contextualized multilingual word embeddings"):
  with members:
    - "cross lingual language model pretraining"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "polyglot semantic role labeling"
    - "xnli evaluating cross lingual sentence representations"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "deep contextualized word representations"
    - "multi source cross lingual model transfer learning what to share"

Connected through hyperedge HE-7 (induced by source paper "deconstructing and reconstructing word embedding algorithms"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "dynamic meta embeddings for improved sentence representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi task learning for sequence tagging an empirical study"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "reconstruction of word embeddings from sub word parameters"
    - "compressing word embeddings via deep compositional code learning"

Connected through hyperedge HE-8 (induced by source paper "low resource sequence labeling via unsupervised multilingual contextualized representations"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural cross lingual named entity recognition with minimal resources"
    - "demystifying neural style transfer"
    - "attention is all you need"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "model transfer for tagging low resource languages using a bilingual dictionary"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"

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)