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: "structural supervision improves learning of non local grammatical dependencies"
  Abstract: State-of-the-art LSTM language models trained on large corpora learn sequential contingencies in impressive detail and have been shown to acquire a number of non-local grammatical dependencies with some success. Here we investigate whether supervision with hierarchical structure enhances learning of a range of grammatical dependencies, a question that has previously been addressed only for subject-verb agreement. Using controlled experimental methods from psycholinguistics, we compare the performance of word-based LSTM models versus two models that represent hierarchical structure and deploy it in left-to-right processing: Recurrent Neural Network Grammars (RNNGs) (Dyer et al., 2016) and a incrementalized version of the Parsing-as-Language-Modeling configuration from Chariak et al., (2016). Models are tested on a diverse range of configurations for two classes of non-local grammatical dependencies in English---Negative Polarity licensing and Filler--Gap Dependencies. Using the same training data across models, we find that structurally-supervised models outperform the LSTM, with the RNNG demonstrating best results on both types of grammatical dependencies and even learning many of the Island Constraints on the filler--gap dependency. Structural supervision thus provides data efficiency advantages over purely string-based training of neural language models in acquiring human-like generalizations about non-local grammatical dependencies.

Connected through hyperedge HE-1 (induced by source paper "compound probabilistic context free grammars for grammar induction"):
  with members:
    - "scalable syntax aware language models using knowledge distillation"
    - "unsupervised recurrent neural network grammars"
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "an imitation learning approach to unsupervised parsing"
    - "text generation with exemplar based adaptive decoding"
    - "controllable paraphrase generation with a syntactic exemplar"
    - "visually grounded neural syntax acquisition"

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"
    - "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"
    - "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:
    - "grammatical analysis of pretrained sentence encoders with acceptability judgments"
    - "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"
    - "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 "quantity doesn t buy quality syntax with neural language models"):
  with members:
    - "assessing bert s syntactic abilities"
    - "cloze driven pretraining of self attention networks"
    - "does bert agree evaluating knowledge of structure dependence through agreement relations"
    - "transformer xl attentive language models beyond a fixed length context"
    - "distinct patterns of syntactic agreement errors in recurrent networks and humans"
    - "neural arithmetic logic units"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning longer term dependencies in rnns with auxiliary losses"

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"
    - "neural language models as psycholinguistic subjects representations of syntactic state"
    - "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 "linguistic analysis of pretrained sentence encoders with acceptability judgments"):
  with members:
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "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"
    - "do rnns learn human like abstract word order preferences"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-7 (induced by source paper "neural network acceptability judgments"):
  with members:
    - "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"
    - "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"

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)