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: "better summarization evaluation with word embeddings for rouge"
  Abstract: ROUGE is a widely adopted, automatic evaluation measure for text summarization. While it has been shown to correlate well with human judgements, it is biased towards surface lexical similarities. This makes it unsuitable for the evaluation of abstractive summarization, or summaries with substantial paraphrasing. We study the effectiveness of word embeddings to overcome this disadvantage of ROUGE. Specifically, instead of measuring lexical overlaps, word embeddings are used to compute the semantic similarity of the words used in summaries instead. Our experimental results show that our proposal is able to achieve better correlations with human judgements when measured with the Spearman and Kendall rank coefficients.

Connected through hyperedge HE-1 (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"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "annotation artifacts in natural language inference data"
    - "generating wikipedia by summarizing long sequences"

Connected through hyperedge HE-2 (induced by source paper "controlling the amount of verbatim copying in abstractive summarization"):
  with members:
    - "sample efficient text summarization using a single pre trained transformer"
    - "biset bi directional selective encoding with template for abstractive summarization"
    - "bertscore evaluating text generation with bert"
    - "unified language model pre training for natural language understanding and generation"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "moverscore text generation evaluating with contextualized embeddings and earth mover distance"):
  with members:
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "bertscore evaluating text generation with bert"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "towards scalable and reliable capsule networks for challenging nlp applications"
    - "linguistic knowledge and transferability of contextual representations"
    - "better rewards yield better summaries learning to summarise without references"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "a simple theoretical model of importance for summarization"

Connected through hyperedge HE-4 (induced by source paper "answers unite unsupervised metrics for reinforced summarization models"):
  with members:
    - "guiding extractive summarization with question answering rewards"
    - "question answering as an automatic evaluation metric for news article summarization"
    - "improving abstraction in text summarization"
    - "bottom up abstractive summarization"
    - "actor critic based training framework for abstractive summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fast abstractive summarization with reinforce selected sentence rewriting"

Connected through hyperedge HE-5 (induced by source paper "a semantic qa based approach for text summarization evaluation"):
  with members:
    - "skip thought vectors"

Connected through hyperedge HE-6 (induced by source paper "word2vec vs dbnary augmenting meteor using vector representations or lexical resources"):
  with members:
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"

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)