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: "syntaxnet models for the conll 2017 shared task"
  Abstract: We describe a baseline dependency parsing system for the CoNLL2017 Shared Task. This system, which we call "ParseySaurus," uses the DRAGNN framework [Kong et al, 2017] to combine transition-based recurrent parsing and tagging with character-based word representations. On the v1.3 Universal Dependencies Treebanks, the new system outpeforms the publicly available, state-of-the-art "Parsey's Cousins" models by 3.47% absolute Labeled Accuracy Score (LAS) across 52 treebanks.

Connected through hyperedge HE-1 (induced by source paper "graph transformer for graph to sequence learning"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "transformer xl attentive language models beyond a fixed length context"
    - "structural neural encoders for amr to text generation"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "factorising amr generation through syntax"
    - "toward abstractive summarization using semantic representations"
    - "graph2seq graph to sequence learning with attention based neural networks"

Connected through hyperedge HE-2 (induced by source paper "char rnn for word stress detection in east slavic languages"):
  with members:
    - "automated word stress detection in russian"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "cross lingual character level neural morphological tagging"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "massively multilingual word embeddings"
    - "bag of tricks for efficient text classification"
    - "multilingual distributed representations without word alignment"

Connected through hyperedge HE-3 (induced by source paper "semantic hypergraphs"):
  with members:
    - "deep contextualized word representations"
    - "globally normalized transition based neural networks"

Connected through hyperedge HE-4 (induced by source paper "syntree2vec an algorithm to augment syntactic hierarchy into word embeddings"):
  with members:
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "modeling order in neural word embeddings at scale"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "on multilingual training of neural dependency parsers"):
  with members:
    - "read tag and parse all at once or fully neural dependency parsing"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "on the relation between dependency distance crossing dependencies and parsing comment on dependency distance a new perspective on syntactic patterns in natural languages by haitao liu et al"):
  with members:
    - "scarcity of crossing dependencies a direct outcome of a specific constraint"
    - "globally normalized transition based neural networks"
    - "non crossing dependencies least effort not grammar"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "crossings as a side effect of dependency lengths"
    - "transition based dependency parsing with stack long short term memory"

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)