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: "grammatical analysis of pretrained sentence encoders with acceptability judgments"
  Abstract: Recent pretrained sentence encoders achieve state of the art results on language understanding tasks, but does this mean they have implicit knowledge of syntactic structures? We introduce a grammatically annotated development set for the Corpus of Linguistic Acceptability (CoLA; Warstadt et al., 2018), which we use to investigate the grammatical knowledge of three pretrained encoders, including the popular OpenAI Transformer (Radford et al., 2018) and BERT (Devlin et al., 2018). We fine-tune these encoders to do acceptability classification over CoLA and compare the models' performance on the annotated analysis set. Some phenomena, e.g. modification by adjuncts, are easy to learn for all models, while others, e.g. long-distance movement, are learned effectively only by models with strong overall performance, and others still, e.g. morphological agreement, are hardly learned by any model.

Connected through hyperedge HE-1 (induced by source paper "sentilr linguistic knowledge enhanced language representation for sentiment analysis"):
  with members:
    - "fine tuning pre trained transformer language models to distantly supervised relation extraction"
    - "mass masked sequence to sequence pre training for language generation"
    - "matching the blanks distributional similarity for relation learning"
    - "ernie enhanced language representation with informative entities"
    - "story ending prediction by transferable bert"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-2 (induced by source paper "blimp a benchmark of linguistic minimal pairs for english"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "representation of constituents in neural language models coordination phrase as a case study"
    - "structural supervision improves learning of non local grammatical dependencies"
    - "investigating bert s knowledge of language five analysis methods with npis"
    - "what do rnn language models learn about filler gap dependencies"
    - "assessing composition in sentence vector representations"
    - "rnns as psycholinguistic subjects syntactic state and grammatical dependency"

Connected through hyperedge HE-3 (induced by source paper "investigating bert s knowledge of language five analysis methods with npis"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "multi task deep neural networks for natural language understanding"
    - "bert and pals projected attention layers for efficient adaptation in multi task learning"
    - "structural supervision improves learning of non local grammatical dependencies"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "assessing composition in sentence vector representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-4 (induced by source paper "modeling german verb argument structures lstms vs humans"):
  with members:
    - "assessing bert s syntactic abilities"
    - "transformer xl attentive language models beyond a fixed length context"
    - "neural language models as psycholinguistic subjects representations of syntactic state"
    - "tabula nearly rasa probing the linguistic knowledge of character level neural language models trained on unsegmented text"
    - "what do rnn language models learn about filler gap dependencies"
    - "distinct patterns of syntactic agreement errors in recurrent networks and humans"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "negated lama birds cannot fly"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "language models as knowledge bases"
    - "transformer xl attentive language models beyond a fixed length context"
    - "probing what different nlp tasks teach machines about function word comprehension"
    - "a bert baseline for the natural questions"
    - "roberta a robustly optimized bert pretraining approach"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"

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)