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: "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
  Abstract: Scientific article summarization is challenging: large, annotated corpora are not available, and the summary should ideally include the article's impacts on research community. This paper provides novel solutions to these two challenges. We 1) develop and release the first large-scale manually-annotated corpus for scientific papers (on computational linguistics) by enabling faster annotation, and 2) propose summarization methods that integrate the authors' original highlights (abstract) and the article's actual impacts on the community (citations), to create comprehensive, hybrid summaries. We conduct experiments to demonstrate the efficacy of our corpus in training data-driven models for scientific paper summarization and the advantage of our hybrid summaries over abstracts and traditional citation-based summaries. Our large annotated corpus and hybrid methods provide a new framework for scientific paper summarization research.

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"
    - "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"
    - "hierarchical neural story generation"

Connected through hyperedge HE-2 (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"
    - "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"
    - "improving abstraction in text summarization"

Connected through hyperedge HE-3 (induced by source paper "talksumm a dataset and scalable annotation method for scientific paper summarization based on conference talks"):
  with members:
    - "the cl scisumm shared task 2018 results and key insights"
    - "data driven summarization of scientific articles"
    - "scientific document summarization via citation contextualization and scientific discourse"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "a supervised approach to extractive summarisation of scientific papers"
    - "get to the point summarization with pointer generator networks"
    - "neural summarization by extracting sentences and words"
    - "what s cookin interpreting cooking videos using text speech and vision"

Connected through hyperedge HE-4 (induced by source paper "plain english summarization of contracts"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "style transfer from non parallel text by cross alignment"
    - "sentence simplification with deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-5 (induced by source paper "a summarization system for scientific documents"):
  with members:
    - "identification of tasks datasets evaluation metrics and numeric scores for scientific leaderboards construction"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "tutorialbank a manually collected corpus for prerequisite chains survey extraction and resource recommendation"
    - "cl scholar the acl anthology knowledge graph miner"
    - "scientific paper summarization using citation summary networks"

Connected through hyperedge HE-6 (induced by source paper "overview and results cl scisumm shared task 2019"):
  with members:
    - "scientific paper summarization using citation summary networks"

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)