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: "transforming question answering datasets into natural language inference datasets"
  Abstract: Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets.

Connected through hyperedge HE-1 (induced by source paper "boolq exploring the surprising difficulty of natural yes no questions"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "annotation artifacts in natural language inference data"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "interpretation of natural language rules in conversational machine reading"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "collecting diverse natural language inference problems for sentence representation evaluation"

Connected through hyperedge HE-2 (induced by source paper "understanding roles and entities datasets and models for natural language inference"):
  with members:
    - "paws paraphrase adversaries from word scrambling"
    - "multi task deep neural networks for natural language understanding"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "large scale qa srl parsing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "enhancing and combining sequential and tree lstm for natural language inference"

Connected through hyperedge HE-3 (induced by source paper "open information extraction from question answer pairs"):
  with members:
    - "neural open information extraction"
    - "opennmt open source toolkit for neural machine translation"
    - "indirect supervision for relation extraction using question answer pairs"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "learning a neural semantic parser from user feedback"
    - "a syntactic neural model for general purpose code generation"
    - "a constrained sequence to sequence neural model for sentence simplification"
    - "recurrent neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "careful selection of knowledge to solve open book question answering"):
  with members:
    - "declarative question answering over knowledge bases containing natural language text with answer set programming"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"
    - "answering science exam questions using query rewriting with background knowledge"
    - "coqa a conversational question answering challenge"
    - "quarel a dataset and models for answering questions about qualitative relationships"
    - "improving machine reading comprehension with general reading strategies"

Connected through hyperedge HE-5 (induced by source paper "kermit generative insertion based modeling for sequences"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "ernie enhanced representation through knowledge integration"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "non autoregressive neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "pentagon at mediqa 2019 multi task learning for filtering and re ranking answers using language inference and question entailment"):
  with members:
    - "a question entailment approach to question answering"
    - "multi task deep neural networks for natural language understanding"
    - "ernie enhanced language representation with informative entities"
    - "lessons from natural language inference in the clinical domain"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a survey on multi task learning"
    - "neural models for information retrieval"

Connected through hyperedge HE-7 (induced by source paper "learning open information extraction of implicit relations from reading comprehension datasets"):
  with members:
    - "a survey on open information extraction"
    - "neural open information extraction"
    - "opennmt open source toolkit for neural machine translation"
    - "newsqa a machine comprehension dataset"
    - "squad 100 000 questions for machine comprehension of text"
    - "getting more out of syntax with props"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "glue a multi task benchmark and analysis platform for natural language understanding"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "the natural language decathlon multitask learning as question answering"
    - "hypothesis only baselines in natural language inference"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "performance impact caused by hidden bias of training data for recognizing textual entailment"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "neural network acceptability judgments"

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)