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: "question answering and question generation as dual tasks"
  Abstract: We study the problem of joint question answering (QA) and question generation (QG) in this paper. #R##N#Our intuition is that QA and QG have intrinsic connections and these two tasks could improve each other. #R##N#On one side, the QA model judges whether the generated question of a QG model is relevant to the answer. #R##N#On the other side, the QG model provides the probability of generating a question given the answer, which is a useful evidence that in turn facilitates QA. #R##N#In this paper we regard QA and QG as dual tasks. #R##N#We propose a training framework that trains the models of QA and QG simultaneously, and explicitly leverages their probabilistic correlation to guide the training process of both models. #R##N#We implement a QG model based on sequence-to-sequence learning, and a QA model based on recurrent neural network. #R##N#As all the components of the QA and QG models are differentiable, all the parameters involved in these two models could be conventionally learned with back propagation. #R##N#We conduct experiments on three datasets. Empirical results show that our training framework improves both QA and QG tasks. #R##N#The improved QA model performs comparably with strong baseline approaches on all three datasets.

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 "reinforcement learning based graph to sequence model for natural question generation"):
  with members:
    - "bidirectional attentive memory networks for question answering over knowledge bases"
    - "learning to generate questions by learningwhat not to generate"
    - "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "deep iterative and adaptive learning for graph neural networks"
    - "unified language model pre training for natural language understanding and generation"
    - "sql to text generation with graph to sequence model"
    - "towards a better metric for evaluating question generation systems"

Connected through hyperedge HE-3 (induced by source paper "addressing semantic drift in question generation for semi supervised question answering"):
  with members:
    - "synthetic qa corpora generation with roundtrip consistency"
    - "learning to generate questions by learningwhat not to generate"
    - "evaluating rewards for question generation models"
    - "unified language model pre training for natural language understanding and generation"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "towards a better metric for evaluating question generation systems"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-4 (induced by source paper "semantic parsing with dual learning"):
  with members:
    - "agentgraph towards universal dialogue management with structured deep reinforcement learning"
    - "a hierarchical decoding model for spoken language understanding from unaligned data"
    - "question generation from sql queries improves neural semantic parsing"
    - "sequence to action end to end semantic graph generation for semantic parsing"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "coarse to fine decoding for neural semantic parsing"
    - "dual supervised learning"

Connected through hyperedge HE-5 (induced by source paper "keeping notes conditional natural language generation with a scratchpad mechanism"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "neural argument generation augmented with externally retrieved evidence"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "learning to ask neural question generation for reading comprehension"
    - "neural text generation a practical guide"

Connected through hyperedge HE-6 (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-7 (induced by source paper "jointly learning semantic parser and natural language generator via dual information maximization"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "coarse to fine decoding for neural semantic parsing"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "code2seq generating sequences from structured representations of code"
    - "triangle generative adversarial networks"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"

Connected through hyperedge HE-8 (induced by source paper "coacor code annotation for code retrieval with reinforcement learning"):
  with members:
    - "comment generation for source code state of the art challenges and opportunities"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "neural network models for paraphrase identification semantic textual similarity natural language inference and question answering"
    - "visual question generation as dual task of visual question answering"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"

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)