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: "automating reading comprehension by generating question and answer pairs"
  Abstract: Neural network-based methods represent the state-of-the-art in question generation from text. Existing work focuses on generating only questions from text without concerning itself with answer generation. Moreover, our analysis shows that handling rare words and generating the most appropriate question given a candidate answer are still challenges facing existing approaches. We present a novel two-stage process to generate question-answer pairs from the text. For the first stage, we present alternatives for encoding the span of the pivotal answer in the sentence using Pointer Networks. In our second stage, we employ sequence to sequence models for question generation, enhanced with rich linguistic features. Finally, global attention and answer encoding are used for generating the question most relevant to the answer. We motivate and linguistically analyze the role of each component in our framework and consider compositions of these. This analysis is supported by extensive experimental evaluations. Using standard evaluation metrics as well as human evaluations, our experimental results validate the significant improvement in the quality of questions generated by our framework over the state-of-the-art. The technique presented here represents another step towards more automated reading comprehension assessment. We also present a live system \footnote{Demo of the system is available at \url{this https URL}.} to demonstrate the effectiveness of our approach.

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 "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"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "neural generation of diverse questions using answer focus contextual and linguistic features"
    - "neural models for key phrase detection and question generation"

Connected through hyperedge HE-3 (induced by source paper "natural question generation with reinforcement learning based graph to sequence model"):
  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"
    - "sql to text generation with graph to sequence model"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "improving neural question generation using answer separation"

Connected through hyperedge HE-4 (induced by source paper "cross lingual training for automatic question generation"):
  with members:
    - "cross lingual language model pretraining"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "cross lingual transfer learning for multilingual task oriented dialog"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised neural machine translation with weight sharing"
    - "learning to ask neural question generation for reading comprehension"
    - "attention is all you need"
    - "a joint model for question answering and question generation"

Connected through hyperedge HE-5 (induced by source paper "paraqg a system for generating questions and answers from paragraphs"):
  with members:
    - "cross lingual training for automatic question generation"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to ask neural question generation for reading comprehension"
    - "attention is all you need"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "evaluating rewards for question generation models"):
  with members:
    - "interpretation of natural language rules in conversational machine reading"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "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"
    - "machine comprehension by text to text neural question generation"
    - "a deep reinforced model for abstractive summarization"
    - "learning functions across many orders of magnitudes"

Connected through hyperedge HE-7 (induced by source paper "question generation from paragraphs a tale of two hierarchical models"):
  with members:
    - "assessing bert s syntactic abilities"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "learning to ask neural question generation for reading comprehension"
    - "visual question generation as dual task of visual question answering"
    - "attention is all you need"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-8 (induced by source paper "semi supervised few shot learning for dual question answer extraction"):
  with members:
    - "neural architectures for open type relation argument extraction"
    - "qa4ie a question answering based framework for information extraction"
    - "two stage synthesis networks for transfer learning in machine comprehension"
    - "attention is all you need"
    - "a joint model for question answering and question generation"
    - "zero shot relation extraction via reading comprehension"
    - "convolutional sequence to sequence learning"
    - "question answering and question generation as dual tasks"

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)