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: "neural language models as psycholinguistic subjects representations of syntactic state"
  Abstract: We deploy the methods of controlled psycholinguistic experimentation to shed light on the extent to which the behavior of neural network language models reflects incremental representations of syntactic state. To do so, we examine model behavior on artificial sentences containing a variety of syntactically complex structures. We test four models: two publicly available LSTM sequence models of English (Jozefowicz et al., 2016; Gulordava et al., 2018) trained on large datasets; an RNNG (Dyer et al., 2016) trained on a small, parsed dataset; and an LSTM trained on the same small corpus as the RNNG. We find evidence that the LSTMs trained on large datasets represent syntactic state over large spans of text in a way that is comparable to the RNNG, while the LSTM trained on the small dataset does not or does so only weakly.

Connected through hyperedge HE-1 (induced by source paper "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "assessing bert s syntactic abilities"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "what do rnn language models learn about filler gap dependencies"
    - "assessing composition in sentence vector representations"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "do language models understand anything on the ability of lstms to understand negative polarity items"

Connected through hyperedge HE-2 (induced by source paper "harnessing the richness of the linguistic signal in predicting pragmatic inferences"):
  with members:
    - "attention is not not explanation"
    - "what syntactic structures block dependencies in rnn language models"
    - "attention is not explanation"
    - "huggingface s transformers state of the art natural language processing"
    - "attention interpretability across nlp tasks"
    - "linguistic knowledge and transferability of contextual representations"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "using priming to uncover the organization of syntactic representations in neural language models"):
  with members:
    - "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"
    - "the emergence of number and syntax units in lstm language models"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "a neural model of adaptation in reading"
    - "what do rnn language models learn about filler gap dependencies"
    - "do language models understand anything on the ability of lstms to understand negative polarity items"
    - "analysis methods in neural language processing a survey"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "probing the phonetic and phonological knowledge of tones in mandarin tts models"):
  with members:
    - "linguistic generalization and compositionality in modern artificial neural networks"
    - "using priming to uncover the organization of syntactic representations in neural language models"
    - "fastspeech fast robust and controllable text to speech"
    - "towards transfer learning for end to end speech synthesis from deep pre trained language models"
    - "representation learning for discovering phonemic tone contours"
    - "dynamic prosody generation for speech synthesis using linguistics driven acoustic embedding selection"
    - "pre training with whole word masking for chinese bert"
    - "waveglow a flow based generative network for speech synthesis"

Connected through hyperedge HE-5 (induced by source paper "hierarchical representation in neural language models suppression and recovery of expectations"):
  with members:
    - "what syntactic structures block dependencies in rnn language models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "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"
    - "what do rnn language models learn about filler gap dependencies"
    - "rnns as psycholinguistic subjects syntactic state and grammatical dependency"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "ordered neurons integrating tree structures into recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "modeling german verb argument structures lstms vs humans"):
  with members:
    - "assessing bert s syntactic abilities"
    - "grammatical analysis of pretrained sentence encoders with acceptability judgments"
    - "transformer xl attentive language models beyond a fixed length context"
    - "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"

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)