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: "rnns as psycholinguistic subjects syntactic state and grammatical dependency"
  Abstract: Recurrent neural networks (RNNs) are the state of the art in sequence modeling for natural language. However, it remains poorly understood what grammatical characteristics of natural language they implicitly learn and represent as a consequence of optimizing the language modeling objective. Here we deploy the methods of controlled psycholinguistic experimentation to shed light on to what extent RNN behavior reflects incremental syntactic state and grammatical dependency representations known to characterize human linguistic behavior. We broadly test two publicly available long short-term memory (LSTM) English sequence models, and learn and test a new Japanese LSTM. We demonstrate that these models represent and maintain incremental syntactic state, but that they do not always generalize in the same way as humans. Furthermore, none of our models learn the appropriate grammatical dependency configurations licensing reflexive pronouns or negative polarity items.

Connected through hyperedge HE-1 (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"
    - "grammatical analysis of pretrained sentence encoders with acceptability judgments"
    - "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"

Connected through hyperedge HE-2 (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"
    - "neural language models as psycholinguistic subjects representations of syntactic state"
    - "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"

Connected through hyperedge HE-3 (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"
    - "neural language models as psycholinguistic subjects representations of syntactic state"
    - "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"
    - "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-4 (induced by source paper "studying the inductive biases of rnns with synthetic variations of natural languages"):
  with members:
    - "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"
    - "do language models understand anything on the ability of lstms to understand negative polarity items"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "are all languages equally hard to language model"
    - "targeted syntactic evaluation of language models"

Connected through hyperedge HE-5 (induced by source paper "representation of constituents in neural language models coordination phrase as a case study"):
  with members:
    - "finding syntax in human encephalography with beam search"
    - "what do rnn language models learn about filler gap dependencies"
    - "colorless green recurrent networks dream hierarchically"
    - "targeted syntactic evaluation of language models"
    - "effective inference for generative neural parsing"
    - "recurrent neural network grammars"
    - "coordination annotation extension in the penn tree bank"
    - "exploring the limits of language modeling"

Connected through hyperedge HE-6 (induced by source paper "structural supervision improves learning of non local grammatical dependencies"):
  with members:
    - "finding syntax in human encephalography with beam search"
    - "what do rnn language models learn about filler gap dependencies"
    - "colorless green recurrent networks dream hierarchically"
    - "targeted syntactic evaluation of language models"
    - "effective inference for generative neural parsing"
    - "recurrent neural network grammars"
    - "exploring the limits of language modeling"
    - "what do recurrent neural network grammars learn about syntax"

Connected through hyperedge HE-7 (induced by source paper "neural network acceptability judgments"):
  with members:
    - "structural supervision improves learning of non local grammatical dependencies"
    - "what do rnn language models learn about filler gap dependencies"
    - "assessing composition in sentence vector representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "targeted syntactic evaluation of language models"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-8 (induced by source paper "do rnns learn human like abstract word order preferences"):
  with members:
    - "what can linguistics and deep learning contribute to each other"
    - "what do rnn language models learn about filler gap dependencies"
    - "colorless green recurrent networks dream hierarchically"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "targeted syntactic evaluation of language models"
    - "do latent tree learning models identify meaningful structure in sentences"
    - "exploring the limits of language modeling"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"

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)