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 models for key phrase detection and question generation"
  Abstract: We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.

Connected through hyperedge HE-1 (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-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 generation of diverse questions using answer focus contextual and linguistic features"

Connected through hyperedge HE-3 (induced by source paper "reinforced dynamic reasoning for conversational question generation"):
  with members:
    - "question generation from sql queries improves neural semantic parsing"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "coqa a conversational question answering challenge"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "quac question answering in context"

Connected through hyperedge HE-4 (induced by source paper "learning to generate questions by learningwhat not to generate"):
  with members:
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "multiresolution graph attention networks for relevance matching"
    - "difficulty controllable question generation for reading comprehension"
    - "improving neural question generation using answer separation"
    - "relational inductive biases deep learning and graph networks"
    - "difficulty controllable generation of reading comprehension questions"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "sequential copying networks"

Connected through hyperedge HE-5 (induced by source paper "does order matter an empirical study on generating multiple keyphrases as a sequence"):
  with members:
    - "keyphrase generation a text summarization struggle"
    - "divgraphpointer a graph pointer network for extracting diverse keyphrases"
    - "keyphrase generation with correlation constraints"
    - "bottom up abstractive summarization"
    - "one size does not fit all generating and evaluating variable number of keyphrases"
    - "semi supervised learning for neural keyphrase generation"
    - "the importance of generation order in language modeling"
    - "deep keyphrase generation"

Connected through hyperedge HE-6 (induced by source paper "automatically extracting subroutine summary descriptions from unstructured comments"):
  with members:
    - "recommendations for datasets for source code summarization"
    - "a neural model for generating natural language summaries of program subroutines"
    - "structured neural summarization"
    - "adapting neural text classification for improved software categorization"
    - "code2seq generating sequences from structured representations of code"
    - "attention is all you need"
    - "deep keyphrase generation"
    - "automatically generating commit messages from diffs using neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "capturing greater context for question generation"):
  with members:
    - "learning to generate questions by learningwhat not to generate"
    - "improving neural question generation using answer separation"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "sequential copying networks"
    - "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"
    - "learning to generate questions by learningwhat not to generate"
    - "recent advances in neural question generation"
    - "improving neural question generation using answer separation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "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)