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: "text summarization techniques a brief survey"
  Abstract: In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.

Connected through hyperedge HE-1 (induced by source paper "human like time series summaries via trend utility estimation"):
  with members:
    - "content selection in data to text systems a survey"
    - "the automatic statistician a relational perspective"
    - "towards ai complete question answering a set of prerequisite toy tasks"

Connected through hyperedge HE-2 (induced by source paper "training language gans from scratch"):
  with members:
    - "the curious case of neural text degeneration"
    - "on accurate evaluation of gans for language generation"
    - "learning to write with cooperative discriminators"
    - "the relativistic discriminator a key element missing from standard gan"
    - "spectral normalization for generative adversarial networks"
    - "universal sentence encoder"
    - "large scale gan training for high fidelity natural image synthesis"
    - "texygen a benchmarking platform for text generation models"

Connected through hyperedge HE-3 (induced by source paper "cooperative generator discriminator networks for abstractive summarization with narrative flow"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "efficient adaptation of pretrained transformers for abstractive summarization"
    - "sample efficient text summarization using a single pre trained transformer"
    - "bertscore evaluating text generation with bert"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"

Connected through hyperedge HE-4 (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"
    - "a survey on neural network based summarization methods"

Connected through hyperedge HE-5 (induced by source paper "ranking sentences from product description bullets for better search"):
  with members:
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "towards a simplified ontology for better e commerce search"
    - "get to the point summarization with pointer generator networks"
    - "graph based neural multi document summarization"
    - "neural extractive summarization with side information"
    - "a deep reinforced model for abstractive summarization"
    - "deep reinforcement learning for dialogue generation"
    - "distraction based neural networks for document summarization"

Connected through hyperedge HE-6 (induced by source paper "extractive summarization of long documents by combining global and local context"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "content selection in deep learning models of summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "a supervised approach to extractive summarisation of scientific papers"
    - "get to the point summarization with pointer generator networks"
    - "scientific article summarization using citation context and article s discourse structure"
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"

Connected through hyperedge HE-7 (induced by source paper "point less more abstractive summarization with pointer generator networks"):
  with members:
    - "improving abstraction in text summarization"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "structure infused copy mechanisms for abstractive summarization"
    - "robust neural abstractive summarization systems and evaluation against adversarial information"
    - "abstractive summarization using attentive neural techniques"
    - "controlling decoding for more abstractive summaries with copy based networks"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "neural abstractive text summarization and fake news detection"):
  with members:
    - "attending to entities for better text understanding"
    - "abstractive text summarization by incorporating reader comments"
    - "end to end alzheimer s disease diagnosis and biomarker identification"
    - "abstractive and extractive text summarization using document context vector and recurrent neural networks"
    - "end to end parkinson disease diagnosis using brain mr images by 3d cnn"
    - "towards explainable nlp a generative explanation framework for text classification"
    - "wikihow a large scale text summarization dataset"
    - "attention is all you need"

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)