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: "from word segmentation to pos tagging for vietnamese"
  Abstract: This paper presents an empirical comparison of two strategies for Vietnamese Part-of-Speech (POS) tagging from unsegmented text: (i) a pipeline strategy where we consider the output of a word segmenter as the input of a POS tagger, and (ii) a joint strategy where we predict a combined segmentation and POS tag for each syllable. We also make a comparison between state-of-the-art (SOTA) feature-based and neural network-based models. On the benchmark Vietnamese treebank (Nguyen et al., 2009), experimental results show that the pipeline strategy produces better scores of POS tagging from unsegmented text than the joint strategy, and the highest accuracy is obtained by using a feature-based model.

Connected through hyperedge HE-1 (induced by source paper "phobert pre trained language models for vietnamese"):
  with members:
    - "etnlp a visual aided systematic approach to select pre trained embeddings for a downstream task"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "cross lingual language model pretraining"
    - "roberta a robustly optimized bert pretraining approach"
    - "bertje a dutch bert model"
    - "xnli evaluating cross lingual sentence representations"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"

Connected through hyperedge HE-2 (induced by source paper "etnlp a visual aided systematic approach to select pre trained embeddings for a downstream task"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "improving topic models with latent feature word representations"
    - "introducing two vietnamese datasets for evaluating semantic models of dis similarity and relatedness"
    - "vncorenlp a vietnamese natural language processing toolkit"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attentive neural network for named entity recognition in vietnamese"
    - "a fast and accurate vietnamese word segmenter"

Connected through hyperedge HE-3 (induced by source paper "emotion recognition for vietnamese social media text"):
  with members:
    - "vietnamese transition based dependency parsing with supertag features"
    - "deep learning versus traditional classifiers on vietnamese students feedback corpus"
    - "decision support with text based emotion recognition deep learning for affective computing"
    - "iest wassa 2018 implicit emotions shared task"
    - "a factoid question answering system for vietnamese"
    - "emotion intensities in tweets"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-4 (induced by source paper "vais hate speech detection system a deep learning based approach for system combination"):
  with members:
    - "roberta a robustly optimized bert pretraining approach"
    - "vncorenlp a vietnamese natural language processing toolkit"
    - "fasttext zip compressing text classification models"
    - "very deep convolutional networks for text classification"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "a neural joint model for vietnamese word segmentation pos tagging and dependency parsing"):
  with members:
    - "error analysis for vietnamese dependency parsing"
    - "vietnamese transition based dependency parsing with supertag features"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "vncorenlp a vietnamese natural language processing toolkit"
    - "lstm easy first dependency parsing with pre trained word embeddings and character level word embeddings in vietnamese"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a fast and accurate vietnamese word segmenter"

Connected through hyperedge HE-6 (induced by source paper "a feature rich vietnamese named entity recognition model"):
  with members:
    - "a fast and accurate vietnamese word segmenter"
    - "the importance of automatic syntactic features in vietnamese named entity recognition"
    - "nnvlp a neural network based vietnamese language processing toolkit"
    - "vietnamese named entity recognition using token regular expressions and bidirectional inference"
    - "named entity recognition with bidirectional lstm cnns"
    - "introduction to the conll 2003 shared task language independent named entity recognition"
    - "introduction to the conll 2002 shared task language independent named entity recognition"

Connected through hyperedge HE-7 (induced by source paper "vncorenlp a vietnamese natural language processing toolkit"):
  with members:
    - "a fast and accurate vietnamese word segmenter"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "a novel neural network model for joint pos tagging and graph based dependency parsing"
    - "an empirical study for vietnamese dependency parsing"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-8 (induced by source paper "a fast and accurate vietnamese word segmenter"):
  with members:
    - "ripple down rules for question answering"
    - "a robust transformation based learning approach using ripple down rules for part of speech tagging"
    - "state of the art vietnamese word segmentation"

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)