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: "an empirical study for vietnamese dependency parsing"
  Abstract: This paper presents an empirical comparison of different dependency parsers for Vietnamese, which has some unusual characteristics such as copula drop and verb serialization. Experimental results show that the neural network-based parsers perform significantly better than the traditional parsers. We report the highest parsing scores published to date for Vietnamese with the labeled attachment score (LAS) at 73.53% and the unlabeled attachment score (UAS) at 80.66%.

Connected through hyperedge HE-1 (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-2 (induced by source paper "lstm easy first dependency parsing with pre trained word embeddings and character level word embeddings in vietnamese"):
  with members:
    - "vietnamese transition based dependency parsing with supertag features"
    - "easy first dependency parsing with hierarchical tree lstms"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "vncorenlp a vietnamese natural language processing toolkit"):
  with members:
    - "a fast and accurate vietnamese word segmenter"
    - "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"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-4 (induced by source paper "a novel neural network model for joint pos tagging and graph based dependency parsing"):
  with members:
    - "neural probabilistic model for non projective mst parsing"
    - "deep multitask learning for semantic dependency parsing"
    - "globally normalized transition based neural networks"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "bi directional attention with agreement for dependency parsing"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "transition based dependency parsing with stack long short term memory"
    - "a primer on neural network models for natural language processing"

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)