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: "vietnamese transition based dependency parsing with supertag features"
  Abstract: In recent years, dependency parsing is a fascinating research topic and has a lot of applications in natural language processing. In this paper, we present an effective approach to improve dependency parsing by utilizing supertag features. We performed experiments with the transition-based dependency parsing approach because it can take advantage of rich features. Empirical evaluation on Vietnamese Dependency Treebank showed that, we achieved an improvement of 18.92% in labeled attachment score with gold supertags and an improvement of 3.57% with automatic supertags.

Connected through hyperedge HE-1 (induced by source paper "a pilot study on multiple choice machine reading comprehension for vietnamese texts"):
  with members:
    - "semeval 2017 task 3 community question answering"
    - "korquad1 0 korean qa dataset for machine reading comprehension"
    - "error analysis for vietnamese named entity recognition on deep neural network models"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "sberquad russian reading comprehension dataset description and analysis"
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "lstm easy first dependency parsing with pre trained word embeddings and character level word embeddings in vietnamese"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "syntax aware neural semantic role labeling with supertags"):
  with members:
    - "polyglot contextual representations improve crosslingual transfer"
    - "polyglot semantic role labeling"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "linguistically informed self attention for semantic role labeling"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "neural semantic role labeling with dependency path embeddings"

Connected through hyperedge HE-3 (induced by source paper "emotion recognition for vietnamese social media text"):
  with members:
    - "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"
    - "from word segmentation to pos tagging for vietnamese"
    - "emotion intensities in tweets"
    - "convolutional neural networks for sentence classification"

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

Connected through hyperedge HE-5 (induced by source paper "lstm easy first dependency parsing with pre trained word embeddings and character level word embeddings in vietnamese"):
  with members:
    - "easy first dependency parsing with hierarchical tree lstms"
    - "an empirical study for vietnamese dependency parsing"
    - "enriching word vectors with subword information"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "transition based dependency parsing with stack long short term memory"
    - "named entity recognition with bidirectional lstm cnns"
    - "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)