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: "verb argument structure alternations in word and sentence embeddings"
  Abstract: Verbs occur in different syntactic environments, or frames. We investigate whether artificial neural networks encode grammatical distinctions necessary for inferring the idiosyncratic frame-selectional properties of verbs. We introduce five datasets, collectively called FAVA, containing in aggregate nearly 10k sentences labeled for grammatical acceptability, illustrating different verbal argument structure alternations. We then test whether models can distinguish acceptable English verb-frame combinations from unacceptable ones using a sentence embedding alone. For converging evidence, we further construct LaVA, a corresponding word-level dataset, and investigate whether the same syntactic features can be extracted from word embeddings. Our models perform reliable classifications for some verbal alternations but not others, suggesting that while these representations do encode fine-grained lexical information, it is incomplete or can be hard to extract. Further, differences between the word- and sentence-level models show that some information present in word embeddings is not passed on to the down-stream sentence embeddings.

Connected through hyperedge HE-1 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings"):
  with members:
    - "a primer in bertology what we know about how bert works"
    - "efficient sentence embedding using discrete cosine transform"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fair is better than sensational man is to doctor as woman is to doctor"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "bart denoising sequence to sequence pre training for natural language generation translation and comprehension"

Connected through hyperedge HE-2 (induced by source paper "probing for semantic classes diagnosing the meaning content of word embeddings"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "semantic structure and interpretability of word embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "conditional generators of words definitions"
    - "factors influencing the surprising instability of word embeddings"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

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 "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-5 (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"
    - "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"
    - "do rnns learn human like abstract word order preferences"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (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"

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)