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: "vncorenlp a vietnamese natural language processing toolkit"
  Abstract: We present an easy-to-use and fast toolkit, namely VnCoreNLP---a Java NLP annotation pipeline for Vietnamese. Our VnCoreNLP supports key natural language processing (NLP) tasks including word segmentation, part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing, and obtains state-of-the-art (SOTA) results for these tasks. We release VnCoreNLP to provide rich linguistic annotations to facilitate research work on Vietnamese NLP. Our VnCoreNLP is open-source under GPL v3, 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"
    - "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"
    - "from word segmentation to pos tagging for vietnamese"

Connected through hyperedge HE-3 (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"
    - "from word segmentation to pos tagging for vietnamese"
    - "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-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"
    - "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"
    - "from word segmentation to pos tagging for vietnamese"

Connected through hyperedge HE-5 (induced by source paper "attentive neural network for named entity recognition in vietnamese"):
  with members:
    - "a feature rich vietnamese named entity recognition model"
    - "a feature based model for nested named entity recognition at vlsp 2018 ner evaluation campaign"
    - "the importance of automatic syntactic features in vietnamese named entity recognition"
    - "nnvlp a neural network based vietnamese language processing toolkit"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "neural machine translation by jointly learning to align and translate"

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)