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: "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
  Abstract: How do neural language models keep track of number agreement between subject and verb? We show that `diagnostic classifiers', trained to predict number from the internal states of a language model, provide a detailed understanding of how, when, and where this information is represented. Moreover, they give us insight into when and where number information is corrupted in cases where the language model ends up making agreement errors. To demonstrate the causal role played by the representations we find, we then use agreement information to influence the course of the LSTM during the processing of difficult sentences. Results from such an intervention reveal a large increase in the language model's accuracy. Together, these results show that diagnostic classifiers give us an unrivalled detailed look into the representation of linguistic information in neural models, and demonstrate that this knowledge can be used to improve their performance.

Connected through hyperedge HE-1 (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"
    - "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"
    - "context free transductions with neural stacks"

Connected through hyperedge HE-2 (induced by source paper "probing what different nlp tasks teach machines about function word comprehension"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "on the convergence of adam and beyond"
    - "the natural language decathlon multitask learning as question answering"
    - "on the evaluation of semantic phenomena in neural machine translation using natural language inference"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "comprehensive supersense disambiguation of english prepositions and possessives"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-3 (induced by source paper "what does bert look at an analysis of bert s attention"):
  with members:
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "attention is not explanation"
    - "linguistic knowledge and transferability of contextual representations"
    - "visualizing attention in transformer based language representation models"

Connected through hyperedge HE-4 (induced by source paper "designing and interpreting probes with control tasks"):
  with members:
    - "linspector multilingual probing tasks for word representations"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "probing what different nlp tasks teach machines about function word comprehension"
    - "linguistic knowledge and transferability of contextual representations"
    - "analyzing learned representations of a deep asr performance prediction model"
    - "assessing composition in sentence vector representations"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "still a pain in the neck evaluating text representations on lexical composition"):
  with members:
    - "semeval 2013 task 4 free paraphrases of noun compounds"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "linguistic knowledge and transferability of contextual representations"
    - "allennlp a deep semantic natural language processing platform"
    - "olive oil is made of olives baby oil is made for babies interpreting noun compounds using paraphrases in a neural model"
    - "deep contextualized word representations"
    - "word sense induction with neural bilm and symmetric patterns"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "tx ray quantifying and explaining model knowledge transfer in un supervised nlp"):
  with members:
    - "episodic memory in lifelong language learning"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "continual lifelong learning with neural networks a review"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "visual interaction with deep learning models through collaborative semantic inference"
    - "universal adversarial triggers for attacking and analyzing nlp"

Connected through hyperedge HE-8 (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"
    - "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"

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)