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: "neural question generation from text a preliminary study"
  Abstract: Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage. Traditional methods mainly use rigid heuristic rules to transform a sentence into related questions. In this work, we propose to apply the neural encoder-decoder model to generate meaningful and diverse questions from natural language sentences. The encoder reads the input text and the answer position, to produce an answer-aware input representation, which is fed to the decoder to generate an answer focused question. We conduct a preliminary study on neural question generation from text with the SQuAD dataset, and the experiment results show that our method can produce fluent and diverse questions.

Connected through hyperedge HE-1 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations"):
  with members:
    - "deep bayesian network for visual question generation"
    - "incorporating visual semantics into sentence representations within a grounded space"
    - "visualizing and measuring the geometry of bert"
    - "lxmert learning cross modality encoder representations from transformers"
    - "bert rediscovers the classical nlp pipeline"
    - "context aware zero shot learning for object recognition"
    - "fusion of detected objects in text for visual question answering"
    - "supervised multimodal bitransformers for classifying images and text"

Connected through hyperedge HE-2 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "pre trained language model representations for language generation"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "defending against neural fake news"
    - "albert a lite bert for self supervised learning of language representations"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"

Connected through hyperedge HE-3 (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"
    - "learning to generate questions by learningwhat not to generate"
    - "question type driven question generation"
    - "multi task learning with language modeling for question generation"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-4 (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"
    - "learning to generate questions by learningwhat not to generate"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "addressing semantic drift in question generation for semi supervised question answering"):
  with members:
    - "synthetic qa corpora generation with roundtrip consistency"
    - "learning to generate questions by learningwhat not to generate"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "unified language model pre training for natural language understanding and generation"):
  with members:
    - "fine tune bert for extractive summarization"
    - "learning and evaluating general linguistic intelligence"
    - "pre trained language model representations for language generation"
    - "mass masked sequence to sequence pre training for language generation"
    - "multi task deep neural networks for natural language understanding"
    - "cloze driven pretraining of self attention networks"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "cross lingual natural language generation via pre training"

Connected through hyperedge HE-7 (induced by source paper "mixture content selection for diverse sequence generation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "mixture models for diverse machine translation tricks of the trade"
    - "deep communicating agents for abstractive summarization"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "bottom up abstractive summarization"
    - "variational recurrent neural machine translation"
    - "a hierarchical latent structure for variational conversation modeling"
    - "hierarchical neural story generation"

Connected through hyperedge HE-8 (induced by source paper "recent advances in neural question generation"):
  with members:
    - "question generation from sql queries improves neural semantic parsing"
    - "towards a better metric for evaluating question generation systems"
    - "improving neural question generation using answer separation"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "automating reading comprehension by generating question and answer pairs"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "neural generation of diverse questions using answer focus contextual and linguistic features"

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)