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: "a unified model for extractive and abstractive summarization using inconsistency loss"
  Abstract: We propose a unified model combining the strength of extractive and abstractive summarization. On the one hand, a simple extractive model can obtain sentence-level attention with high ROUGE scores but less readable. On the other hand, a more complicated abstractive model can obtain word-level dynamic attention to generate a more readable paragraph. In our model, sentence-level attention is used to modulate the word-level attention such that words in less attended sentences are less likely to be generated. Moreover, a novel inconsistency loss function is introduced to penalize the inconsistency between two levels of attentions. By end-to-end training our model with the inconsistency loss and original losses of extractive and abstractive models, we achieve state-of-the-art ROUGE scores while being the most informative and readable summarization on the CNN/Daily Mail dataset in a solid human evaluation.

Connected through hyperedge HE-1 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization"):
  with members:
    - "evaluating discourse in structured text representations"
    - "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"
    - "text summarization with pretrained encoders"
    - "bottom up abstractive summarization"
    - "toward abstractive summarization using semantic representations"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "structure infused copy mechanisms for abstractive summarization"
    - "a discourse aware attention model for abstractive summarization of long documents"

Connected through hyperedge HE-2 (induced by source paper "selective attention encoders by syntactic graph convolutional networks for document summarization"):
  with members:
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "bottom up abstractive summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "structure infused copy mechanisms for abstractive summarization"
    - "neural document summarization by jointly learning to score and select sentences"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "selective encoding for abstractive sentence summarization"
    - "graph convolutional encoders for syntax aware neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks"):
  with members:
    - "fine tune bert for extractive summarization"
    - "jointly extracting and compressing documents with summary state representations"
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "mass masked sequence to sequence pre training for language generation"
    - "generating summaries with topic templates and structured convolutional decoders"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "unified language model pre training for natural language understanding and generation"
    - "deep communicating agents for abstractive summarization"

Connected through hyperedge HE-4 (induced by source paper "multi style generative reading comprehension"):
  with members:
    - "learning and evaluating general linguistic intelligence"
    - "a deep cascade model for multi document reading comprehension"
    - "densely connected attention propagation for reading comprehension"
    - "the natural language decathlon multitask learning as question answering"
    - "bottom up abstractive summarization"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "duorc towards complex language understanding with paraphrased reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "neural text summarization a critical evaluation"):
  with members:
    - "what comes next extractive summarization by next sentence prediction"
    - "neural extractive text summarization with syntactic compression"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "annotation artifacts in natural language inference data"
    - "generating wikipedia by summarizing long sequences"
    - "abstractive summarization of reddit posts with multi level memory networks"

Connected through hyperedge HE-6 (induced by source paper "select and attend towards controllable content selection in text generation"):
  with members:
    - "toward unsupervised text content manipulation"
    - "towards content transfer through grounded text generation"
    - "step by step separating planning from realization in neural data to text generation"
    - "global encoding for abstractive summarization"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "bottom up abstractive summarization"
    - "show control and tell a framework for generating controllable and grounded captions"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "summary level training of sentence rewriting for abstractive summarization"):
  with members:
    - "fine tune bert for extractive summarization"
    - "efficient adaptation of pretrained transformers for abstractive summarization"
    - "bertscore evaluating text generation with bert"
    - "pretraining based natural language generation for text summarization"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "neural extractive text summarization with syntactic compression"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-8 (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"

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)