human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "learning to ask neural question generation for reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "dual supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "closed loop matters dual regression networks for single image super resolution")
  Member "neural question generation from text a preliminary study":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "semi supervised qa with generative domain adaptive nets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "description based text classification with reinforcement learning")
  Member "pointing the unknown words":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "asking questions the human way scalable question answer generation from text corpus")
  Member "attentive pooling networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cut based graph learning networks to discover compositional structure of sequential video data")
  Member "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "asking questions the human way scalable question answer generation from text corpus")
  Member "incorporating copying mechanism in sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "recent advances and challenges in task oriented dialog system")

Question: Please tell me which class the source paper 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)