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: "neural network acceptability judgments"
  Abstract: This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.'s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions.

Connected through hyperedge HE-1 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "knowledge distillation from internal representations"
    - "small and practical bert models for sequence labeling"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "cloze driven pretraining of self attention networks"

Connected through hyperedge HE-2 (induced by source paper "the microsoft toolkit of multi task deep neural networks for natural language understanding"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "multi task deep neural networks for natural language understanding"
    - "smart robust and efficient fine tuning for pre trained natural language models through principled regularized optimization"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "freelb enhanced adversarial training for language understanding"

Connected through hyperedge HE-3 (induced by source paper "bert of theseus compressing bert by progressive module replacing"):
  with members:
    - "are sixteen heads really better than one"
    - "reducing transformer depth on demand with structured dropout"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "q bert hessian based ultra low precision quantization of bert"
    - "well read students learn better on the importance of pre training compact models"
    - "deep double descent where bigger models and more data hurt"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-4 (induced by source paper "on the comparability of pre trained language models"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "defending against neural fake news"
    - "albert a lite bert for self supervised learning of language representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "roberta a robustly optimized bert pretraining approach"
    - "dual co matching network for multi choice reading comprehension"
    - "a simple method for commonsense reasoning"

Connected through hyperedge HE-5 (induced by source paper "unilmv2 pseudo masked language models for unified language model pre training"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "albert a lite bert for self supervised learning of language representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "text summarization with pretrained encoders"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-6 (induced by source paper "freelb enhanced adversarial training for natural language understanding"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "adversarial attacks on copyright detection systems"
    - "multi task deep neural networks for natural language understanding"
    - "albert a lite bert for self supervised learning of language representations"
    - "robust neural machine translation with doubly adversarial inputs"
    - "roberta a robustly optimized bert pretraining approach"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"

Connected through hyperedge HE-7 (induced by source paper "fine tuning pretrained language models weight initializations data orders and early stopping"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "show your work improved reporting of experimental results"
    - "albert a lite bert for self supervised learning of language representations"
    - "freelb enhanced adversarial training for language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-8 (induced by source paper "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "multi task deep neural networks for natural language understanding"
    - "explicit bias discovery in visual question answering models"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "from recognition to cognition visual commonsense reasoning"
    - "annotation artifacts in natural language inference data"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "gender bias in coreference resolution"

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)