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: "pointing the unknown words"
  Abstract: The problem of rare and unknown words is an important issue that can potentially influence the performance of many NLP systems, including both the traditional count-based and the deep learning models. We propose a novel way to deal with the rare and unseen words for the neural network models using attention. Our model uses two softmax layers in order to predict the next word in conditional language models: one predicts the location of a word in the source sentence, and the other predicts a word in the shortlist vocabulary. At each time-step, the decision of which softmax layer to use choose adaptively made by an MLP which is conditioned on the context.~We motivate our work from a psychological evidence that humans naturally have a tendency to point towards objects in the context or the environment when the name of an object is not known.~We observe improvements on two tasks, neural machine translation on the Europarl English to French parallel corpora and text summarization on the Gigaword dataset using our proposed model.

Connected through hyperedge HE-1 (induced by source paper "asking questions the human way scalable question answer generation from text corpus"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "generating question answer hierarchies"
    - "improving neural question generation using world knowledge"
    - "the curious case of neural text degeneration"
    - "learning to generate questions by learningwhat not to generate"
    - "question type driven question generation"
    - "multi task learning with language modeling for question generation"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-2 (induced by source paper "two huge title and keyword generation corpora of research articles"):
  with members:
    - "neural keyphrase generation via reinforcement learning with adaptive rewards"
    - "sentiment analysis of czech texts an algorithmic survey"
    - "a review of keyphrase extraction"
    - "efficiency metrics for data driven models a text summarization case study"
    - "keyphrase generation a multi aspect survey"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "data driven summarization of scientific articles"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "gasp generating abstracts of scientific papers from abstracts of cited papers"):
  with members:
    - "bottom up abstractive summarization"
    - "a survey on neural network based summarization methods"
    - "construction of the literature graph in semantic scholar"
    - "opennmt open source toolkit for neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-4 (induced by source paper "learning to memorize in neural task oriented dialogue systems"):
  with members:
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "zero shot dialog generation with cross domain latent actions"
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "the natural language decathlon multitask learning as question answering"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "global locally self attentive dialogue state tracker"

Connected through hyperedge HE-5 (induced by source paper "denoising based sequence to sequence pre training for text generation"):
  with members:
    - "pre trained language model representations for language generation"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "mass masked sequence to sequence pre training for language generation"
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "cross lingual language model pretraining"
    - "unified language model pre training for natural language understanding and generation"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-6 (induced by source paper "mixture content selection for diverse sequence generation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "mixture models for diverse machine translation tricks of the trade"
    - "deep communicating agents for abstractive summarization"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "bottom up abstractive summarization"
    - "variational recurrent neural machine translation"
    - "a hierarchical latent structure for variational conversation modeling"
    - "hierarchical neural story generation"

Connected through hyperedge HE-7 (induced by source paper "adversarial domain adaptation for machine reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "adversarial domain adaptation for duplicate question detection"
    - "multi task learning with sample re weighting for machine reading comprehension"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "towards vqa models that can read"):
  with members:
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "disentangling language and knowledge in task oriented dialogs"
    - "rosetta large scale system for text detection and recognition in images"
    - "dvqa understanding data visualizations via question answering"
    - "figureqa an annotated figure dataset for visual reasoning"

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)