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 generation from a knowledge base with web exploration"
  Abstract: Question generation from a knowledge base (KB) is the task of generating questions related to the domain of the input KB. We propose a system for generating fluent and natural questions from a KB, which significantly reduces the human effort by leveraging massive web resources. In more detail, a seed question set is first generated by applying a small number of hand-crafted templates on the input KB, then more questions are retrieved by iteratively forming already obtained questions as search queries into a standard search engine, before finally questions are selected by estimating their fluency and domain relevance. Evaluated by human graders on 500 random-selected triples from Freebase, questions generated by our system are judged to be more fluent than those of \newcite{serban-EtAl:2016:P16-1} by human graders.

Connected through hyperedge HE-1 (induced by source paper "easy to hard leveraging simple questions for complex question generation"):
  with members:
    - "multimodal differential network for visual question generation"
    - "learning to ask questions in open domain conversational systems with typed decoders"
    - "the web as a knowledge base for answering complex questions"
    - "toward abstractive summarization using semantic representations"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "learning to ask neural question generation for reading comprehension"
    - "get to the point summarization with pointer generator networks"
    - "creativity generating diverse questions using variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "natural language generation with neural variational models"):
  with members:
    - "probabilistic natural language generation with wasserstein autoencoders"
    - "learning to ask neural question generation for reading comprehension"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "latent variable dialogue models and their diversity"
    - "adversarial learning for neural dialogue generation"
    - "variational attention for sequence to sequence models"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-3 (induced by source paper "question answering and question generation as dual tasks"):
  with members:
    - "learning to ask neural question generation for reading comprehension"
    - "dual supervised learning"
    - "neural question generation from text a preliminary study"
    - "semi supervised qa with generative domain adaptive nets"
    - "pointing the unknown words"
    - "attentive pooling networks"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "incorporating copying mechanism in sequence to sequence 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)