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: "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
  Abstract: We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our minimal feature set into the dynamic-programming framework of Huang and Sagae (2010) and Kuhlmann et al. (2011) to produce the first implementation of worst-case O(n^3) exact decoders for arc-hybrid and arc-eager transition systems. With our minimal features, we also present O(n^3) global training methods. Finally, using ensembles including our new parsers, we achieve the best unlabeled attachment score reported (to our knowledge) on the Chinese Treebank and the "second-best-in-class" result on the English Penn Treebank.

Connected through hyperedge HE-1 (induced by source paper "parsing as pretraining"):
  with members:
    - "assessing bert s syntactic abilities"
    - "viable dependency parsing as sequence labeling"
    - "better faster stronger sequence tagging constituent parsers"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "designing and interpreting probes with control tasks"
    - "the non utility of structural features in bilstm based dependency parsers"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "linguistic knowledge and transferability of contextual representations"

Connected through hyperedge HE-2 (induced by source paper "a systematic study of leveraging subword information for learning word representations"):
  with members:
    - "morphological word embeddings"
    - "learning semantic representations for novel words leveraging both form and context"
    - "generalizing word embeddings using bag of subwords"
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"
    - "from characters to words to in between do we capture morphology"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "a graph based model for joint chinese word segmentation and dependency parsing"):
  with members:
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "searching for effective neural extractive summarization what works and what s next"
    - "pre training with whole word masking for chinese bert"
    - "state of the art chinese word segmentation with bi lstms"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
    - "decoupled weight decay regularization"
    - "joint pos tagging and dependency parsing with transition based neural networks"

Connected through hyperedge HE-4 (induced by source paper "the non utility of structural features in bilstm based dependency parsers"):
  with members:
    - "recursive subtree composition in lstm based dependency parsing"
    - "what s going on in neural constituency parsers an analysis"
    - "transition based parsing with lighter feed forward networks"
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"
    - "global transition based non projective dependency parsing"
    - "deep rnns encode soft hierarchical syntax"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "an investigation of the interactions between pre trained word embeddings character models and pos tags in dependency parsing"

Connected through hyperedge HE-5 (induced by source paper "viable dependency parsing as sequence labeling"):
  with members:
    - "constituent parsing as sequence labeling"
    - "scheduled multi task learning from syntax to translation"
    - "sequence to sequence learning as beam search optimization"
    - "dependency parsing as head selection"
    - "bidirectional lstm crf models for sequence tagging"
    - "transition based dependency parsing with stack long short term memory"
    - "grammar as a foreign language"

Connected through hyperedge HE-6 (induced by source paper "scene graph parsing as dependency parsing"):
  with members:
    - "a transition based directed acyclic graph parser for ucca"
    - "neural amr sequence to sequence models for parsing and generation"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"
    - "robust incremental neural semantic graph parsing"
    - "recurrent multimodal interaction for referring image segmentation"
    - "one model to learn them all"
    - "attention correctness in neural image captioning"

Connected through hyperedge HE-7 (induced by source paper "two local models for neural constituent parsing"):
  with members:
    - "in order transition based constituent parsing"
    - "a minimal span based neural constituency parser"
    - "neural probabilistic model for non projective mst parsing"
    - "improving neural parsing by disentangling model combination and reranking effects"
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"
    - "recurrent neural network grammars"
    - "globally normalized transition based neural networks"
    - "incremental parsing with minimal features using bi directional lstm"

Connected through hyperedge HE-8 (induced by source paper "dependency based hybrid trees for semantic parsing"):
  with members:
    - "learning cross lingual distributed logical representations for semantic parsing"
    - "universal semantic parsing"
    - "learning structured natural language representations for semantic parsing"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "language to logical form with neural attention"
    - "data recombination for neural semantic parsing"
    - "neural architectures for named entity recognition"
    - "getting more out of syntax with props"

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)