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 "natural question generation with reinforcement learning based graph to sequence model"):
  Abstract: Natural question generation (QG) aims to generate questions from a passage and an answer. In this paper, we propose a novel reinforcement learning (RL) based graph-to-sequence (Graph2Seq) model for QG. Our model consists of a Graph2Seq generator where a novel Bidirectional Gated Graph Neural Network is proposed to embed the passage, and a hybrid evaluator with a mixed objective combining both cross-entropy and RL losses to ensure the generation of syntactically and semantically valid text. The proposed model outperforms previous state-of-the-art methods by a large margin on the SQuAD dataset.

Includes members:
  Member "bidirectional attentive memory networks for question answering over knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation")
  Member "learning to generate questions by learningwhat not to generate":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "asking questions the human way scalable question answer generation from text corpus")
  Member "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation")
  Member "reinforcement learning based text style transfer without parallel training corpus":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation")
  Member "sql to text generation with graph to sequence model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation")
  Member "putting the horse before the cart a generator evaluator framework for question generation from text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "description based text classification with reinforcement learning")
  Member "exploiting rich syntactic information for semantic parsing with graph to sequence model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation")
  Member "improving neural question generation using answer separation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "asking questions the human way scalable question answer generation from text corpus")

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)