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 survey on neural network based summarization methods"
  Abstract: Automatic text summarization, the automated process of shortening a text while reserving the main ideas of the document(s), is a critical research area in natural language processing. The aim of this literature review is to survey the recent work on neural-based models in automatic text summarization. We examine in detail ten state-of-the-art neural-based summarizers: five abstractive models and five extractive models. In addition, we discuss the related techniques that can be applied to the summarization tasks and present promising paths for future research in neural-based summarization.

Connected through hyperedge HE-1 (induced by source paper "gasp generating abstracts of scientific papers from abstracts of cited papers"):
  with members:
    - "bottom up abstractive summarization"
    - "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"
    - "pointing the unknown words"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-2 (induced by source paper "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"):
  with members:
    - "discrimination in the age of algorithms"
    - "fair and diverse dpp based data summarization"
    - "equity of attention amortizing individual fairness in rankings"
    - "simplicity creates inequity implications for fairness stereotypes and interpretability"
    - "fair clustering through fairlets"
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "selection problems in the presence of implicit bias"
    - "text summarization techniques a brief survey"

Connected through hyperedge HE-3 (induced by source paper "from web crawled text to project descriptions automatic summarizing of social innovation projects"):
  with members:
    - "extractive text summarization using neural networks"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "recent trends in deep learning based natural language processing"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "neural summarization by extracting sentences and words"
    - "sequence to sequence rnns for text summarization"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-4 (induced by source paper "global voices crossing borders in automatic news summarization"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "bottom up abstractive summarization"
    - "text summarization techniques a brief survey"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "fasttext zip compressing text classification models"
    - "bag of tricks for efficient text classification"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-5 (induced by source paper "abstractive summarization using attentive neural techniques"):
  with members:
    - "actor critic based training framework for abstractive summarization"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "efficient summarization with read again and copy mechanism"
    - "get to the point summarization with pointer generator networks"
    - "convolutional sequence to sequence learning"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"

Connected through hyperedge HE-6 (induced by source paper "fairness preserving text summarzation"):
  with members:
    - "fair clustering through fairlets"
    - "trump vs hillary what went viral during the 2016 us presidential election"
    - "fa ir a fair top k ranking algorithm"
    - "exposing the probabilistic causal structure of discrimination"
    - "how to be fair and diverse"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "budgeted influence maximization for multiple products"
    - "lexrank graph based lexical centrality as salience in text summarization"

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)