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: "emrqa a large corpus for question answering on electronic medical records"
  Abstract: We propose a novel methodology to generate domain-specific large-scale question answering (QA) datasets by re-purposing existing annotations for other NLP tasks. We demonstrate an instance of this methodology in generating a large-scale QA dataset for electronic medical records by leveraging existing expert annotations on clinical notes for various NLP tasks from the community shared i2b2 datasets. The resulting corpus (emrQA) has 1 million question-logical form and 400,000+ question-answer evidence pairs. We characterize the dataset and explore its learning potential by training baseline models for question to logical form and question to answer mapping.

Connected through hyperedge HE-1 (induced by source paper "practical annotation strategies for question answering datasets"):
  with members:
    - "mrqa 2019 shared task evaluating generalization in reading comprehension"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "what makes reading comprehension questions easier"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "adversarial examples for evaluating reading comprehension systems"

Connected through hyperedge HE-2 (induced by source paper "generating scientific question answering corpora from q a forums"):
  with members:
    - "a question entailment approach to question answering"
    - "biomedical question answering via weighted neural network passage retrieval"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "pacrr a position aware neural ir model for relevance matching"
    - "a deep relevance matching model for ad hoc retrieval"

Connected through hyperedge HE-3 (induced by source paper "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"):
  with members:
    - "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"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "the web as a knowledge base for answering complex questions"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-4 (induced by source paper "text to sql generation for question answering on electronic medical records"):
  with members:
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "editing based sql query generation for cross domain context dependent questions"
    - "table2answer read the database and answer without sql"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "the natural language decathlon multitask learning as question answering"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-5 (induced by source paper "a translate edit model for natural language question to sql query generation on multi relational healthcare data"):
  with members:
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "table2answer read the database and answer without sql"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "the natural language decathlon multitask learning as question answering"
    - "neural abstractive text summarization with sequence to sequence models a survey"
    - "bottom up abstractive summarization"
    - "improving text to sql evaluation methodology"

Connected through hyperedge HE-6 (induced by source paper "towards domain adaptation from limited data for question answering using deep neural networks"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "multi task deep neural networks for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "adversarial domain adaptation for machine reading comprehension"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "pubmedqa a dataset for biomedical research question answering"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "probing biomedical embeddings from language models"
    - "interpretation of natural language rules in conversational machine reading"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bag of words as target for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "adapting and evaluating a deep learning language model for clinical why question answering"):
  with members:
    - "publicly available clinical bert embeddings"
    - "roberta a robustly optimized bert pretraining approach"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "squad 100 000 questions for machine comprehension of text"

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)