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 generation of diverse questions using answer focus contextual and linguistic features"
  Abstract: Question Generation is the task of automatically creating questions from textual input. In this work we present a new Attentional Encoder--Decoder Recurrent Neural Network model for automatic question generation. Our model incorporates linguistic features and an additional sentence embedding to capture meaning at both sentence and word levels. The linguistic features are designed to capture information related to named entity recognition, word case, and entity coreference resolution. In addition our model uses a copying mechanism and a special answer signal that enables generation of numerous diverse questions on a given sentence. Our model achieves state of the art results of 19.98 Bleu_4 on a benchmark Question Generation dataset, outperforming all previously published results by a significant margin. A human evaluation also shows that these added features improve the quality of the generated questions.

Connected through hyperedge HE-1 (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-2 (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 models for key phrase detection and question generation"

Connected through hyperedge HE-3 (induced by source paper "generating a common question from multiple documents using multi source encoder decoder models"):
  with members:
    - "passage re ranking with bert"
    - "asking clarifying questions in open domain information seeking conversations"
    - "a deep cascade model for multi document reading comprehension"
    - "multi style generative reading comprehension"
    - "deep communicating agents for abstractive summarization"
    - "input combination strategies for multi source transformer decoder"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"

Connected through hyperedge HE-4 (induced by source paper "improving neural question generation using world knowledge"):
  with members:
    - "difficulty controllable question generation for reading comprehension"
    - "improving neural question generation using answer separation"
    - "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"
    - "pointing the unknown words"
    - "newsqa a machine comprehension dataset"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-5 (induced by source paper "learning to answer by learning to ask getting the best of gpt 2 and bert worlds"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "recent advances in neural question generation"
    - "towards a better metric for evaluating question generation systems"
    - "improving neural question generation using answer separation"
    - "hierarchical neural story generation"
    - "learning to ask neural question generation for reading comprehension"
    - "learning to paraphrase for question answering"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "holistic multi modal memory network for movie question answering"):
  with members:
    - "compositional attention networks for machine reasoning"
    - "attention on attention architectures for visual question answering vqa"
    - "tvqa localized compositional video question answering"
    - "decoupled novel object captioner"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "movie question answering remembering the textual cues for layered visual contents"
    - "learning two branch neural networks for image text matching tasks"
    - "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)