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: "generating distractors for reading comprehension questions from real examinations"
  Abstract: We investigate the task of distractor generation for multiple choice reading comprehension questions from examinations. In contrast to all previous works, we do not aim at preparing words or short phrases distractors, instead, we endeavor to generate longer and semantic-rich distractors which are closer to distractors in real reading comprehension from examinations. Taking a reading comprehension article, a pair of question and its correct option as input, our goal is to generate several distractors which are somehow related to the answer, consistent with the semantic context of the question and have some trace in the article. We propose a hierarchical encoder-decoder framework with static and dynamic attention mechanisms to tackle this task. Specifically, the dynamic attention can combine sentence-level and word-level attention varying at each recurrent time step to generate a more readable sequence. The static attention is to modulate the dynamic attention not to focus on question irrelevant sentences or sentences which contribute to the correct option. Our proposed framework outperforms several strong baselines on the first prepared distractor generation dataset of real reading comprehension questions. For human evaluation, compared with those distractors generated by baselines, our generated distractors are more functional to confuse the annotators.

Connected through hyperedge HE-1 (induced by source paper "good better best textual distractors generation for multi choice vqa via policy gradient"):
  with members:
    - "augmenting model robustness with transformation invariant attacks"
    - "from recognition to cognition visual commonsense reasoning"
    - "generating natural language adversarial examples"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "which training methods for gans do actually converge"
    - "darts deceiving autonomous cars with toxic signs"
    - "dancin seq2seq fooling text classifiers with adversarial text example generation"
    - "asking the difficult questions goal oriented visual question generation via intermediate rewards"

Connected through hyperedge HE-2 (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"
    - "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"
    - "machine comprehension by text to text neural question generation"

Connected through hyperedge HE-3 (induced by source paper "improving question generation with to the point context"):
  with members:
    - "improving neural question generation using answer separation"
    - "difficulty controllable generation of reading comprehension questions"
    - "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"
    - "faithful to the original fact aware neural abstractive summarization"
    - "machine comprehension by text to text neural question generation"
    - "pointing the unknown words"

Connected through hyperedge HE-4 (induced by source paper "co attention hierarchical network generating coherent long distractors for reading comprehension"):
  with members:
    - "race large scale reading comprehension dataset from examinations"
    - "get to the point summarization with pointer generator networks"
    - "improving semantic relevance for sequence to sequence learning of chinese social media text summarization"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "dynamic coattention networks for question answering"
    - "a hierarchical neural autoencoder for paragraphs and documents"

Connected through hyperedge HE-5 (induced by source paper "robust reading comprehension with linguistic constraints via posterior regularization"):
  with members:
    - "robust machine comprehension models via adversarial training"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"
    - "on calibration of modern neural networks"
    - "the narrativeqa reading comprehension challenge"
    - "adversarial examples for evaluating reading comprehension systems"
    - "squad 100 000 questions for machine comprehension of text"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "the goldilocks principle reading children s books with explicit memory representations"

Connected through hyperedge HE-6 (induced by source paper "title guided encoding for keyphrase generation"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "learning feature representations for keyphrase extraction"
    - "semi supervised learning for neural keyphrase generation"
    - "a unified query based generative model for question generation and question answering"
    - "opennmt open source toolkit for neural machine translation"
    - "deep keyphrase generation"
    - "a joint model for question answering and question generation"
    - "diversity driven attention model for query based abstractive summarization"

Connected through hyperedge HE-7 (induced by source paper "difficulty controllable generation of reading comprehension questions"):
  with members:
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "automating reading comprehension by generating question and answer pairs"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "title guided encoding for keyphrase generation"
    - "quase sequence editing under quantifiable guidance"
    - "learning to ask neural question generation for reading comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "neural question generation from text a preliminary study"

Connected through hyperedge HE-8 (induced by source paper "difficulty controllable question generation for reading comprehension"):
  with members:
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "title guided encoding for keyphrase generation"
    - "style transfer from non parallel text by cross alignment"
    - "learning to ask neural question generation for reading comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "get to the point summarization with pointer generator networks"
    - "squad 100 000 questions for machine comprehension of text"

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)