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: "learning to generate questions by learningwhat not to generate"
  Abstract: Automatic question generation is an important technique that can improve the training of question answering, help chatbots to start or continue a conversation with humans, and provide assessment materials for educational purposes. Existing neural question generation models are not sufficient mainly due to their inability to properly model the process of how each word in the question is selected, i.e., whether repeating the given passage or being generated from a vocabulary. In this paper, we propose our Clue Guided Copy Network for Question Generation (CGC-QG), which is a sequence-to-sequence generative model with copying mechanism, yet employing a variety of novel components and techniques to boost the performance of question generation. In CGC-QG, we design a multi-task labeling strategy to identify whether a question word should be copied from the input passage or be generated instead, guiding the model to learn the accurate boundaries between copying and generation. Furthermore, our input passage encoder takes as input, among a diverse range of other features, the prediction made by a clue word predictor, which helps identify whether each word in the input passage is a potential clue to be copied into the target question. The clue word predictor is designed based on a novel application of Graph Convolutional Networks onto a syntactic dependency tree representation of each passage, thus being able to predict clue words only based on their context in the passage and their relative positions to the answer in the tree. We jointly train the clue prediction as well as question generation with multi-task learning and a number of practical strategies to reduce the complexity. Extensive evaluations show that our model significantly improves the performance of question generation and out-performs all previous state-of-the-art neural question generation models by a substantial margin.

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"
    - "question type driven question generation"
    - "multi task learning with language modeling for question generation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what does bert look at an analysis of bert s attention"

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"
    - "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"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"

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"
    - "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"
    - "speaker follower models for vision and language navigation"

Connected through hyperedge HE-4 (induced by source paper "natural question generation with reinforcement learning based graph to sequence model"):
  with members:
    - "bidirectional attentive memory networks for question answering over knowledge bases"
    - "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "sql to text generation with graph to sequence model"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "improving neural question generation using answer separation"
    - "graph2seq graph to sequence learning with attention based neural networks"

Connected through hyperedge HE-5 (induced by source paper "two birds one stone a simple unified model for text generation from structured and unstructured data"):
  with members:
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "improving neural question generation using answer separation"
    - "sequential copying networks"
    - "learning to ask neural question generation for reading comprehension"
    - "on the state of the art of evaluation in neural language models"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "neural question generation from text a preliminary study"
    - "question answering and question generation as dual tasks"

Connected through hyperedge HE-6 (induced by source paper "extended answer and uncertainty aware neural question generation"):
  with members:
    - "improving neural question generation using answer separation"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "learning to ask neural question generation for reading comprehension"
    - "a unified query based generative model for question generation and question answering"
    - "neural question generation from text a preliminary study"
    - "get to the point summarization with pointer generator networks"
    - "pointing the unknown words"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-7 (induced by source paper "capturing greater context for question generation"):
  with members:
    - "improving neural question generation using answer separation"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "sequential copying networks"
    - "neural models for key phrase detection and question generation"
    - "learning to ask neural question generation for reading comprehension"
    - "neural question generation from text a preliminary study"
    - "get to the point summarization with pointer generator networks"
    - "machine comprehension by text to text neural question generation"

Connected through hyperedge HE-8 (induced by source paper "let me know what to ask interrogative word aware question generation"):
  with members:
    - "unsupervised question answering by cloze translation"
    - "recent advances in neural question generation"
    - "improving neural question generation using answer separation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural models for key phrase detection and question generation"
    - "learning to ask neural question generation for reading comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention is all you need"

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)