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 fast and accurate vietnamese word segmenter"
  Abstract: We propose a novel approach to Vietnamese word segmentation. Our approach is based on the Single Classification Ripple Down Rules methodology (Compton and Jansen, 1990), where rules are stored in an exception structure and new rules are only added to correct segmentation errors given by existing rules. Experimental results on the benchmark Vietnamese treebank show that our approach outperforms previous state-of-the-art approaches JVnSegmenter, vnTokenizer, DongDu and UETsegmenter in terms of both accuracy and performance speed. Our code is open-source and available at: this https URL

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"
    - "from word segmentation to pos tagging for vietnamese"

Connected through hyperedge HE-3 (induced by source paper "etnlp a toolkit for extraction evaluation and visualization of pre trained word embeddings"):
  with members:
    - "improving topic models with latent feature word representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "enriching word vectors with subword information"
    - "document classification by inversion of distributed language representations"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (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"
    - "from word segmentation to pos tagging for vietnamese"

Connected through hyperedge HE-5 (induced by source paper "a feature rich vietnamese named entity recognition model"):
  with members:
    - "the importance of automatic syntactic features in vietnamese named entity recognition"
    - "from word segmentation to pos tagging for vietnamese"
    - "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-6 (induced by source paper "vncorenlp a vietnamese natural language processing toolkit"):
  with members:
    - "from word segmentation to pos tagging for vietnamese"
    - "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-7 (induced by source paper "a feature based model for nested named entity recognition at vlsp 2018 ner evaluation campaign"):
  with members:
    - "a feature rich vietnamese named entity recognition model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "introduction to the conll 2002 shared task language independent named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "from word segmentation to pos tagging for vietnamese"):
  with members:
    - "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"
    - "bidirectional lstm crf models for sequence tagging"

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)