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: "training with exploration improves a greedy stack lstm parser"
  Abstract: We adapt the greedy Stack-LSTM dependency parser of Dyer et al. (2015) to support a training-with-exploration procedure using dynamic oracles(Goldberg and Nivre, 2013) instead of cross-entropy minimization. This form of training, which accounts for model predictions at training time rather than assuming an error-free action history, improves parsing accuracies for both English and Chinese, obtaining very strong results for both languages. We discuss some modifications needed in order to get training with exploration to work well for a probabilistic neural-network.

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 "rewarding smatch transition based amr parsing with reinforcement learning"):
  with members:
    - "amr parsing as sequence to graph transduction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an amr aligner tuned by transition based parser"
    - "amr parsing as graph prediction with latent alignment"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "amr parsing using stack lstms"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"

Connected through hyperedge HE-3 (induced by source paper "graph to graph transformer for transition based dependency parsing"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "assessing bert s syntactic abilities"
    - "recurrent graph syntax encoder for neural machine translation"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "incorporating syntactic and semantic information in word embeddings using graph convolutional networks"
    - "cross sentence n ary relation extraction with graph lstms"

Connected through hyperedge HE-4 (induced by source paper "discontinuous constituency parsing with a stack free transition system and a dynamic oracle"):
  with members:
    - "unlexicalized transition based discontinuous constituency parsing"
    - "dynamic oracles for top down and in order shift reduce constituent parsing"
    - "a minimal span based neural constituency parser"
    - "arc swift a novel transition system for dependency parsing"
    - "non projective dependency parsing with non local transitions"
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "incremental parsing with minimal features using bi directional lstm"

Connected through hyperedge HE-5 (induced by source paper "technical notes syntax aware representation learning with pointer networks"):
  with members:
    - "viable dependency parsing as sequence labeling"
    - "non projective dependency parsing via latent heads representation lhr"
    - "stack pointer networks for dependency parsing"
    - "scheduled multi task learning from syntax to translation"
    - "easy first dependency parsing with hierarchical tree lstms"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "transition based dependency parsing with stack long short term memory"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-6 (induced by source paper "left to right dependency parsing with pointer networks"):
  with members:
    - "arc swift a novel transition system for dependency parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"
    - "bi directional attention with agreement for dependency parsing"
    - "transition based dependency parsing with stack long short term memory"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (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"
    - "dropout with expectation linear regularization"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"

Connected through hyperedge HE-8 (induced by source paper "linguistically informed self attention for semantic role labeling"):
  with members:
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "end to end neural coreference resolution"
    - "learning structured text representations"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "frame semantic parsing with softmax margin segmental rnns and a syntactic scaffold"

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)