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: "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
  Abstract: Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scale question-answer corpora available. In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the question-generation model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear comparable in quality to real human-generated questions.

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"
    - "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-2 (induced by source paper "introduction to neural network based approaches for question answering over knowledge graphs"):
  with members:
    - "pretrained transformers for simple question answering over knowledge graphs"
    - "learning to generalize from sparse and underspecified rewards"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "practical semantic parsing for spoken language understanding"
    - "question generation from sql queries improves neural semantic parsing"
    - "towards a question answering system over the semantic web"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"

Connected through hyperedge HE-3 (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-4 (induced by source paper "information maximizing visual question generation"):
  with members:
    - "neural baby talk"
    - "fast decoding in sequence models using discrete latent variables"
    - "visual curiosity learning to ask questions to learn visual recognition"
    - "learning to ask neural question generation for reading comprehension"
    - "inferring and executing programs for visual reasoning"
    - "visual question generation as dual task of visual question answering"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-5 (induced by source paper "unsupervised common question generation from multiple documents using reinforced contrastive coordinator"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "generating a common question from multiple documents using multi source encoder decoder models"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "asking clarifying questions in open domain information seeking conversations"
    - "a deep cascade model for multi document reading comprehension"
    - "towards coherent and cohesive long form text generation"
    - "multi style generative reading comprehension"

Connected through hyperedge HE-6 (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-7 (induced by source paper "deep exemplar networks for vqa and vqg"):
  with members:
    - "multimodal differential network for visual question generation"
    - "differential attention for visual question answering"
    - "learning to ask neural question generation for reading comprehension"
    - "creativity generating diverse questions using variational autoencoders"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "hadamard product for low rank bilinear pooling"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "learning to compose neural networks for question answering"

Connected through hyperedge HE-8 (induced by source paper "keeping notes conditional natural language generation with a scratchpad mechanism"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "neural argument generation augmented with externally retrieved evidence"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "learning to ask neural question generation for reading comprehension"
    - "neural text generation a practical guide"

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)