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: "do language models understand anything on the ability of lstms to understand negative polarity items"
  Abstract: In this paper, we attempt to link the inner workings of a neural language model to linguistic theory, focusing on a complex phenomenon well discussed in formal linguis- tics: (negative) polarity items. We briefly discuss the leading hypotheses about the licensing contexts that allow negative polarity items and evaluate to what extent a neural language model has the ability to correctly process a subset of such constructions. We show that the model finds a relation between the licensing context and the negative polarity item and appears to be aware of the scope of this context, which we extract from a parse tree of the sentence. With this research, we hope to pave the way for other studies linking formal linguistics to deep learning.

Connected through hyperedge HE-1 (induced by source paper "linguistic knowledge and transferability of contextual representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "what can linguistics and deep learning contribute to each other"
    - "what do rnn language models learn about filler gap dependencies"
    - "what s going on in neural constituency parsers an analysis"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "why self attention a targeted evaluation of neural machine translation architectures"

Connected through hyperedge HE-2 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "what do rnn language models learn about filler gap dependencies"
    - "rule induction for global explanation of trained models"
    - "understanding convolutional neural networks for text classification"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation"

Connected through hyperedge HE-3 (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-4 (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"
    - "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"
    - "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"

Connected through hyperedge HE-5 (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"
    - "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"

Connected through hyperedge HE-6 (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"
    - "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"
    - "a neural model of adaptation in reading"
    - "what do rnn language models learn about filler gap dependencies"
    - "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-7 (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"
    - "grammatical analysis of pretrained sentence encoders with acceptability judgments"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "the emergence of number and syntax units in lstm language models"):
  with members:
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "what do rnn language models learn about filler gap dependencies"
    - "distinct patterns of syntactic agreement errors in recurrent networks and humans"
    - "colorless green recurrent networks dream hierarchically"
    - "learning to generate reviews and discovering sentiment"
    - "targeted syntactic evaluation of language models"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"

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)