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: "bi directional attention with agreement for dependency parsing"
  Abstract: We develop a novel bi-directional attention model for dependency parsing, which learns to agree on headword predictions from the forward and backward parsing directions. The parsing procedure for each direction is formulated as sequentially querying the memory component that stores continuous headword embeddings. The proposed parser makes use of {\it soft} headword embeddings, allowing the model to implicitly capture high-order parsing history without dramatically increasing the computational complexity. We conduct experiments on English, Chinese, and 12 other languages from the CoNLL 2006 shared task, showing that the proposed model achieves state-of-the-art unlabeled attachment scores on 6 languages.

Connected through hyperedge HE-1 (induced by source paper "glyce glyph vectors for chinese character representations"):
  with members:
    - "toward fast and accurate neural chinese word segmentation with multi criteria learning"
    - "chinese ner using lattice lstm"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "deep contextualized word representations"
    - "state of the art chinese word segmentation with bi lstms"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "head driven phrase structure grammar parsing on penn treebank"):
  with members:
    - "lattice based transformer encoder for neural machine translation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what s going on in neural constituency parsers an analysis"
    - "two local models for neural constituent parsing"
    - "exploring recombination for efficient decoding of neural machine translation"
    - "multilingual constituency parsing with self attention and pre training"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "global greedy dependency parsing"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "deep contextualized word representations"
    - "learning word vectors for 157 languages"
    - "stack pointer networks for dependency parsing"

Connected through hyperedge HE-4 (induced by source paper "on extended long short term memory and dependent bidirectional recurrent neural network"):
  with members:
    - "lstm a search space odyssey"
    - "squeeze and excitation networks"
    - "convolutional sequence to sequence learning"
    - "visualizing and understanding recurrent networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "grammar as a foreign language"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-5 (induced by source paper "left to right dependency parsing with pointer networks"):
  with members:
    - "arc swift a novel transition system for dependency parsing"
    - "training with exploration improves a greedy stack lstm parser"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"
    - "transition based dependency parsing with stack long short term memory"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "stack pointer networks for dependency parsing"):
  with members:
    - "neural probabilistic model for non projective mst parsing"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "unsupervised ranking model for entity coreference resolution"
    - "globally normalized transition based neural networks"
    - "training with exploration improves a greedy stack lstm parser"
    - "dropout with expectation linear regularization"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-7 (induced by source paper "neural probabilistic model for non projective mst parsing"):
  with members:
    - "deep biaffine attention for neural dependency parsing"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "unsupervised ranking model for entity coreference resolution"
    - "globally normalized transition based neural networks"
    - "training with exploration improves a greedy stack lstm parser"
    - "dependency parsing as head selection"
    - "dropout with expectation linear regularization"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-8 (induced by source paper "dependency parsing with dilated iterated graph cnns"):
  with members:
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "convolutional sequence to sequence learning"
    - "globally normalized transition based neural networks"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "neural machine translation in linear time"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"
    - "wavenet a generative model for raw audio"

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)