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: "a qualitative comparison of coqa squad 2 0 and quac"
  Abstract: We compare three new datasets for question answering: SQuAD 2.0, QuAC, and CoQA, along several of their new features: (1) unanswerable questions, (2) multi-turn interactions, and (3) abstractive answers. We show that the datasets provide complementary coverage of the first two aspects, but weak coverage of the third. Because of the datasets' structural similarity, a single extractive model can be easily adapted to any of the datasets and we show improved baseline results on both SQuAD 2.0 and CoQA. Despite the similarity, models trained on one dataset are ineffective on another dataset, but we find moderate performance improvement through pretraining. To encourage cross-evaluation, we release code for conversion between datasets at this https URL .

Connected through hyperedge HE-1 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards"):
  with members:
    - "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering"
    - "compositional questions do not necessitate multi hop reasoning"
    - "question answering is a format when is it useful"
    - "understanding dataset design choices for multi hop reasoning"

Connected through hyperedge HE-2 (induced by source paper "fquad french question answering dataset"):
  with members:
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "multilingual question answering from formatted text applied to conversational agents"
    - "mlqa evaluating cross lingual extractive question answering"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-3 (induced by source paper "generating question answer hierarchies"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "the curious case of neural text degeneration"
    - "learning to write with cooperative discriminators"
    - "difficulty controllable question generation for reading comprehension"
    - "know what you don t know unanswerable questions for squad"
    - "improving neural question generation using answer separation"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "reinforced dynamic reasoning for conversational question generation"):
  with members:
    - "question generation from sql queries improves neural semantic parsing"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "coqa a conversational question answering challenge"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "quac question answering in context"
    - "flowqa grasping flow in history for conversational machine comprehension"

Connected through hyperedge HE-5 (induced by source paper "attentive history selection for conversational question answering"):
  with members:
    - "bert with history answer embedding for conversational question answering"
    - "answer interaction in non factoid question answering systems"
    - "multi task deep neural networks for natural language understanding"
    - "user intent prediction in information seeking conversations"
    - "a deep look into neural ranking models for information retrieval"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-6 (induced by source paper "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"):
  with members:
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "sql to text generation with graph to sequence model"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "contextualized non local neural networks for sequence learning"
    - "coqa a conversational question answering challenge"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "open domain question answering using early fusion of knowledge bases and text"

Connected through hyperedge HE-7 (induced by source paper "an empirical study of content understanding in conversational question answering"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "analyzing compositionality sensitivity of nli models"
    - "coqa a conversational question answering challenge"
    - "drcd a chinese machine reading comprehension dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"

Connected through hyperedge HE-8 (induced by source paper "frustratingly easy natural question answering"):
  with members:
    - "span selection pre training for question answering"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "sg net syntax guided machine reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "a bert baseline for the natural questions"
    - "roberta a robustly optimized bert pretraining approach"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

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)