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: "scibert pretrained contextualized embeddings for scientific text"
  Abstract: Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained contextualized embedding model based on BERT (Devlin et al., 2018) to address the lack of high-quality, large-scale labeled scientific data. SciBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks. We evaluate on a suite of tasks including sequence tagging, sentence classification and dependency parsing, with datasets from a variety of scientific domains. We demonstrate statistically significant improvements over BERT and achieve new state-of-the-art results on several of these tasks.

Connected through hyperedge HE-1 (induced by source paper "snippext semi supervised opinion mining with augmented data"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "voyageur an experiential travel search engine"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "subjective databases"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "augmenting data with mixup for sentence classification an empirical study"
    - "scibert a pretrained language model for scientific text"
    - "combining mixmatch and active learning for better accuracy with fewer labels"

Connected through hyperedge HE-2 (induced by source paper "navigation based candidate expansion and pretrained language models for citation recommendation"):
  with members:
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "passage re ranking with bert"
    - "a scalable hybrid research paper recommender system for microsoft academic"
    - "scibert a pretrained language model for scientific text"
    - "roberta a robustly optimized bert pretraining approach"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "content based citation recommendation"

Connected through hyperedge HE-3 (induced by source paper "agatha automatic graph mining and transformer based hypothesis generation approach"):
  with members:
    - "scibert a pretrained language model for scientific text"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "pytorch biggraph a large scale graph embedding system"
    - "large scale validation of hypothesis generation systems via candidate ranking"
    - "are abstracts enough for hypothesis generation"
    - "attention is all you need"
    - "billion scale similarity search with gpus"

Connected through hyperedge HE-4 (induced by source paper "rapid adaptation of bert for information extraction on domain specific business documents"):
  with members:
    - "navigation based candidate expansion and pretrained language models for citation recommendation"
    - "scibert a pretrained language model for scientific text"
    - "bert for joint intent classification and slot filling"
    - "joint slot filling and intent detection via capsule neural networks"
    - "a bi model based rnn semantic frame parsing model for intent detection and slot filling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "leveraging sentence level information with encoder lstm for natural language understanding"
    - "leveraging sentence level information with encoder lstm for semantic slot filling"

Connected through hyperedge HE-5 (induced by source paper "parsing early modern english for linguistic search"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert a pretrained language model for scientific text"
    - "probing biomedical embeddings from language models"
    - "extending a parser to distant domains using a few dozen partially annotated examples"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence tagging with bidirectional language models"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-6 (induced by source paper "data mining in clinical trial text transformers for classification and question answering tasks"):
  with members:
    - "huggingface s transformers state of the art natural language processing"
    - "a corpus with multi level annotations of patients interventions and outcomes to support language processing for medical literature"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "squad 100 000 questions for machine comprehension of text"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "cooperative generator discriminator networks for abstractive summarization with narrative flow"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "efficient adaptation of pretrained transformers for abstractive summarization"
    - "sample efficient text summarization using a single pre trained transformer"
    - "bertscore evaluating text generation with bert"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "discourse aware neural rewards for coherent text generation"

Connected through hyperedge HE-8 (induced by source paper "unsupervised domain adaptation of contextualized embeddings for sequence labeling"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "learning and evaluating general linguistic intelligence"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "scibert a pretrained language model for scientific text"
    - "using similarity measures to select pretraining data for ner"
    - "unsupervised data augmentation for consistency training"
    - "evaluating historical text normalization systems how well do they generalize"
    - "deep contextualized word representations"

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)