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 "recent advances in neural question generation"):
  Abstract: Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition. These trends point to NQG as a bellwether for NLP, about how human intelligence embodies the skills of curiosity and integration. #R##N#We present a comprehensive survey of neural question generation, examining the corpora, methodologies, and evaluation methods. From this, we elaborate on what we see as emerging on NQG's trend: in terms of the learning paradigms, input modalities, and cognitive levels considered by NQG. We end by pointing out the potential directions ahead.

Includes members:
  Member "question generation from sql queries improves neural semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "introduction to neural network based approaches for question answering over knowledge graphs")
  Member "towards a better metric for evaluating question generation systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "asking questions the human way scalable question answer generation from text corpus")
  Member "soft layer specific multi task summarization with entailment and question generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to multi task learn for better neural machine translation")
  Member "zero shot question generation from knowledge graphs for unseen predicates and entity types":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation")
  Member "automating reading comprehension by generating question and answer pairs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation")
  Member "harvesting paragraph level question answer pairs from wikipedia":
    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 "neural generation of diverse questions using answer focus contextual and linguistic features":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised common question generation from multiple documents using reinforced contrastive coordinator")

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)