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 joint model for question answering and question generation"
  Abstract: We propose a generative machine comprehension model that learns jointly to ask and answer questions based on documents. The proposed model uses a sequence-to-sequence framework that encodes the document and generates a question (answer) given an answer (question). Significant improvement in model performance is observed empirically on the SQuAD corpus, confirming our hypothesis that the model benefits from jointly learning to perform both tasks. We believe the joint model's novelty offers a new perspective on machine comprehension beyond architectural engineering, and serves as a first step towards autonomous information seeking.

Connected through hyperedge HE-1 (induced by source paper "asking questions the human way scalable question answer generation from text corpus"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "generating question answer hierarchies"
    - "improving neural question generation using world knowledge"
    - "the curious case of neural text degeneration"
    - "learning to generate questions by learningwhat not to generate"
    - "question type driven question generation"
    - "multi task learning with language modeling for question generation"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-2 (induced by source paper "information maximizing visual question generation"):
  with members:
    - "neural baby talk"
    - "fast decoding in sequence models using discrete latent variables"
    - "visual curiosity learning to ask questions to learn visual recognition"
    - "learning to ask neural question generation for reading comprehension"
    - "inferring and executing programs for visual reasoning"
    - "visual question generation as dual task of visual question answering"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-3 (induced by source paper "recent advances in neural question generation"):
  with members:
    - "question generation from sql queries improves neural semantic parsing"
    - "towards a better metric for evaluating question generation systems"
    - "improving neural question generation using answer separation"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "automating reading comprehension by generating question and answer pairs"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "neural generation of diverse questions using answer focus contextual and linguistic features"

Connected through hyperedge HE-4 (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"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "cross lingual training for automatic question generation"):
  with members:
    - "cross lingual language model pretraining"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "automating reading comprehension by generating question and answer pairs"
    - "cross lingual transfer learning for multilingual task oriented dialog"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised neural machine translation with weight sharing"
    - "learning to ask neural question generation for reading comprehension"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "generating highly relevant questions"):
  with members:
    - "improving neural question generation using answer separation"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "learning to ask neural question generation for reading comprehension"
    - "stochastic answer networks for machine reading comprehension"
    - "neural question generation from text a preliminary study"
    - "get to the point summarization with pointer generator networks"
    - "question answering and question generation as dual tasks"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "semi supervised few shot learning for dual question answer extraction"):
  with members:
    - "neural architectures for open type relation argument extraction"
    - "automating reading comprehension by generating question and answer pairs"
    - "qa4ie a question answering based framework for information extraction"
    - "two stage synthesis networks for transfer learning in machine comprehension"
    - "attention is all you need"
    - "zero shot relation extraction via reading comprehension"
    - "convolutional sequence to sequence learning"
    - "question answering and question generation as dual tasks"

Connected through hyperedge HE-8 (induced by source paper "title guided encoding for keyphrase generation"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "learning feature representations for keyphrase extraction"
    - "generating distractors for reading comprehension questions from real examinations"
    - "semi supervised learning for neural keyphrase generation"
    - "a unified query based generative model for question generation and question answering"
    - "opennmt open source toolkit for neural machine translation"
    - "deep keyphrase generation"
    - "diversity driven attention model for query based abstractive summarization"

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)