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: "machine comprehension by text to text neural question generation"
  Abstract: We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard maximum likelihood training, we fine-tune the model using policy gradient techniques to maximize several rewards that measure question quality. Most notably, one of these rewards is the performance of a question-answering system. We motivate question generation as a means to improve the performance of question answering systems. Our model is trained and evaluated on the recent question-answering dataset SQuAD.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "learning to ask unanswerable questions for machine reading comprehension"):
  with members:
    - "unified language model pre training for natural language understanding and generation"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "know what you don t know unanswerable questions for squad"
    - "improving neural question generation using answer separation"
    - "improving machine reading comprehension with general reading strategies"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "learning to ask neural question generation for reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "ask to learn a study on curiosity driven question generation"):
  with members:
    - "synthetic qa corpora generation with roundtrip consistency"
    - "question answering as an automatic evaluation metric for news article summarization"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "evaluating rewards for question generation models"
    - "unified language model pre training for natural language understanding and generation"
    - "analyzing uncertainty in neural machine translation"
    - "towards a better metric for evaluating question generation systems"

Connected through hyperedge HE-5 (induced by source paper "unsupervised question answering by cloze translation"):
  with members:
    - "evaluating rewards for question generation models"
    - "cross lingual language model pretraining"
    - "unsupervised statistical machine translation"
    - "multiple attribute text style transfer"
    - "efficient and robust question answering from minimal context over documents"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "what makes reading comprehension questions easier"

Connected through hyperedge HE-6 (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-7 (induced by source paper "let s ask again refine network for automatic question generation"):
  with members:
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "towards a better metric for evaluating question generation systems"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "improving neural question generation using answer separation"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "sequential copying networks"
    - "learning to ask neural question generation for reading comprehension"

Connected through hyperedge HE-8 (induced by source paper "interconnected question generation with coreference alignment and conversation flow modeling"):
  with members:
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "generating distractors for reading comprehension questions from real examinations"
    - "coqa a conversational question answering challenge"
    - "difficulty controllable generation of reading comprehension questions"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "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"

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)