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: "towards a better metric for evaluating question generation systems"
  Abstract: There has always been criticism for using $n$-gram based similarity metrics, such as BLEU, NIST, etc, for evaluating the performance of NLG systems. However, these metrics continue to remain popular and are recently being used for evaluating the performance of systems which automatically generate questions from documents, knowledge graphs, images, etc. Given the rising interest in such automatic question generation (AQG) systems, it is important to objectively examine whether these metrics are suitable for this task. In particular, it is important to verify whether such metrics used for evaluating AQG systems focus on answerability of the generated question by preferring questions which contain all relevant information such as question type (Wh-types), entities, relations, etc. In this work, we show that current automatic evaluation metrics based on $n$-gram similarity do not always correlate well with human judgments about answerability of a question. To alleviate this problem and as a first step towards better evaluation metrics for AQG, we introduce a scoring function to capture answerability and show that when this scoring function is integrated with existing metrics, they correlate significantly better with human judgments. The scripts and data developed as a part of this work are made publicly available at this https URL

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"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"

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"
    - "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"
    - "speaker follower models for vision and language navigation"

Connected through hyperedge HE-3 (induced by source paper "recent advances in neural question generation"):
  with members:
    - "question generation from sql queries improves neural semantic parsing"
    - "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"
    - "neural models for key phrase detection and question generation"

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"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-5 (induced by source paper "re evaluating adem a deeper look at scoring dialogue responses"):
  with members:
    - "deep text classification can be fooled"
    - "neural approaches to conversational ai"
    - "towards building large scale multimodal domain aware conversation systems"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "adversarial learning for neural dialogue generation"
    - "end to end conversation modeling track in dstc6"
    - "adversarial examples for evaluating reading comprehension systems"
    - "generating natural adversarial examples"

Connected through hyperedge HE-6 (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"
    - "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"
    - "visual question generation as dual task of visual question answering"

Connected through hyperedge HE-7 (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"
    - "improving neural question generation using answer separation"
    - "hierarchical neural story generation"
    - "neural generation of diverse questions using answer focus contextual and linguistic features"
    - "learning to ask neural question generation for reading comprehension"
    - "learning to paraphrase for question answering"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "handling divergent reference texts when evaluating table to text generation"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "object hallucination in image captioning"
    - "identifying semantic divergences in parallel text without annotations"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "improving slot filling performance with attentive neural networks on dependency structures"
    - "get to the point summarization with pointer generator networks"
    - "challenges in data to document generation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"

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)