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: "faithful to the original fact aware neural abstractive summarization"
  Abstract: Unlike extractive summarization, abstractive summarization has to fuse different parts of the source text, which inclines to create fake facts. Our preliminary study reveals nearly 30% of the outputs from a state-of-the-art neural summarization system suffer from this problem. While previous abstractive summarization approaches usually focus on the improvement of informativeness, we argue that faithfulness is also a vital prerequisite for a practical abstractive summarization system. To avoid generating fake facts in a summary, we leverage open information extraction and dependency parse technologies to extract actual fact descriptions from the source text. The dual-attention sequence-to-sequence framework is then proposed to force the generation conditioned on both the source text and the extracted fact descriptions. Experiments on the Gigaword benchmark dataset demonstrate that our model can greatly reduce fake summaries by 80%. Notably, the fact descriptions also bring significant improvement on informativeness since they often condense the meaning of the source text.

Connected through hyperedge HE-1 (induced by source paper "boosting factual correctness of abstractive summarization with knowledge graph"):
  with members:
    - "passage re ranking with bert"
    - "evaluating the factual consistency of abstractive text summarization"
    - "make lead bias in your favor a simple and effective method for news summarization"
    - "assessing the factual accuracy of generated text"
    - "text summarization with pretrained encoders"
    - "unified language model pre training for natural language understanding and generation"
    - "optimizing the factual correctness of a summary a study of summarizing radiology reports"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-2 (induced by source paper "abstractive snippet generation"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "generating wikipedia by summarizing long sequences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "opennmt open source toolkit for neural machine translation"
    - "query based abstractive summarization using neural networks"
    - "learning paraphrastic sentence embeddings from back translated bitext"

Connected through hyperedge HE-3 (induced by source paper "distilling the knowledge of bert for text generation"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "towards making the most of bert in neural machine translation"
    - "mask predict parallel decoding of conditional masked language models"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "pretraining based natural language generation for text summarization"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "patient knowledge distillation for bert model compression"

Connected through hyperedge HE-4 (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-5 (induced by source paper "text generation with exemplar based adaptive decoding"):
  with members:
    - "retrieve and refine improved sequence generation models for dialogue"
    - "backpropagating through structured argmax using a spigot"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "rational recurrences"
    - "bottom up abstractive summarization"
    - "learning joint semantic parsers from disjoint data"
    - "learning neural templates for text generation"
    - "contextual parameter generation for universal neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "joint parsing and generation for abstractive summarization"):
  with members:
    - "analyzing sentence fusion in abstractive summarization"
    - "controlling the amount of verbatim copying in abstractive summarization"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "generating wikipedia by summarizing long sequences"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-7 (induced by source paper "evaluating the factual consistency of abstractive text summarization"):
  with members:
    - "fine tune bert for extractive summarization"
    - "bigpatent a large scale dataset for abstractive and coherent summarization"
    - "pretraining based natural language generation for text summarization"
    - "assessing the factual accuracy of generated text"
    - "text summarization with pretrained encoders"
    - "neural text summarization a critical evaluation"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"

Connected through hyperedge HE-8 (induced by source paper "improving the similarity measure of determinantal point processes for extractive multi document summarization"):
  with members:
    - "parabank monolingual bitext generation and sentential paraphrasing via lexically constrained neural machine translation"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "ranking sentences for extractive summarization with reinforcement learning"

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.IR(Information Retrieval)