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: "scientific article summarization using citation context and article s discourse structure"
  Abstract: We propose a summarization approach for scientific articles which takes advantage of citation-context and the document discourse model. While citations have been previously used in generating scientific summaries, they lack the related context from the referenced article and therefore do not accurately reflect the article's content. Our method overcomes the problem of inconsistency between the citation summary and the article's content by providing context for each citation. We also leverage the inherent scientific article's discourse for producing better summaries. We show that our proposed method effectively improves over existing summarization approaches (greater than 30% improvement over the best performing baseline) in terms of \textsc{Rouge} scores on TAC2014 scientific summarization dataset. While the dataset we use for evaluation is in the biomedical domain, most of our approaches are general and therefore adaptable to other domains.

Connected through hyperedge HE-1 (induced by source paper "citation text generation"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "bertscore evaluating text generation with bert"
    - "defending against neural fake news"
    - "gorc a large contextual citation graph of academic papers"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "content based citation recommendation"

Connected through hyperedge HE-2 (induced by source paper "pretrained language models for sequential sentence classification"):
  with members:
    - "scibert a pretrained language model for scientific text"
    - "roberta a robustly optimized bert pretraining approach"
    - "unified language model pre training for natural language understanding and generation"
    - "hierarchical neural networks for sequential sentence classification in medical scientific abstracts"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (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"
    - "text summarization techniques a brief survey"
    - "get to the point summarization with pointer generator networks"
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"

Connected through hyperedge HE-4 (induced by source paper "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"):
  with members:
    - "the cl scisumm shared task 2018 results and key insights"
    - "scientific document summarization via citation contextualization and scientific discourse"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "a supervised approach to extractive summarisation of scientific papers"
    - "contextualizing citations for scientific summarization using word embeddings and domain knowledge"
    - "get to the point summarization with pointer generator networks"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

Connected through hyperedge HE-5 (induced by source paper "neural related work summarization with a joint context driven attention mechanism"):
  with members:
    - "neural extractive summarization with side information"
    - "node2vec scalable feature learning for networks"
    - "neural summarization by extracting sentences and words"
    - "attsum joint learning of focusing and summarization with neural attention"
    - "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"
    - "end to end attention based large vocabulary speech recognition"

Connected through hyperedge HE-6 (induced by source paper "gorc a large contextual citation graph of academic papers"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "a scalable hybrid research paper recommender system for microsoft academic"
    - "a context aware citation recommendation model with bert and graph convolutional networks"
    - "roberta a robustly optimized bert pretraining approach"
    - "content based citation recommendation"
    - "deep contextualized word representations"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "construction of the literature graph in semantic scholar"

Connected through hyperedge HE-7 (induced by source paper "structural scaffolds for citation intent classification in scientific publications"):
  with members:
    - "scibert pretrained contextualized embeddings for scientific text"
    - "syntactic scaffolds for semantic structures"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "neural multi task learning for citation function and provenance"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "contextualizing citations for scientific summarization using word embeddings and domain knowledge"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"

Connected through hyperedge HE-8 (induced by source paper "a novel ilp framework for summarizing content with high lexical variety"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "scientific document summarization via citation contextualization and scientific discourse"
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "automatic summarization of student course feedback"
    - "an improved phrase based approach to annotating and summarizing student course responses"

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)