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: "read tag and parse all at once or fully neural dependency parsing"
  Abstract: We present a dependency parser implemented as a single deep neural network that reads orthographic representations of words and directly generates dependencies and their labels. Unlike typical approaches to parsing, the model doesn't require part-of-speech (POS) tagging of the sentences. With proper regularization and additional supervision achieved with multitask learning we reach state-of-the-art performance on Slavic languages from the Universal Dependencies treebank: with no linguistic features other than characters, our parser is as accurate as a transition- based system trained on perfect POS tags.

Connected through hyperedge HE-1 (induced by source paper "what s going on in neural constituency parsers an analysis"):
  with members:
    - "n gram language modeling using recurrent neural network estimation"
    - "a minimal span based neural constituency parser"
    - "investigating how well contextual features are captured by bi directional recurrent neural network models"
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"
    - "recurrent neural network grammars"
    - "neural architectures for named entity recognition"
    - "character based neural machine translation"
    - "improved transition based parsing by modeling characters instead of words with lstms"

Connected through hyperedge HE-2 (induced by source paper "on multilingual training of neural dependency parsers"):
  with members:
    - "syntaxnet models for the conll 2017 shared task"
    - "deep biaffine attention for neural dependency parsing"
    - "globally normalized transition based neural networks"
    - "dependency parsing as head selection"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "exploring the limits of language modeling"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "many languages one parser"

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)