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: "data driven summarization of scientific articles"
  Abstract: Data-driven approaches to sequence-to-sequence modelling have been successfully applied to short text summarization of news articles. Such models are typically trained on input-summary pairs consisting of only a single or a few sentences, partially due to limited availability of multi-sentence training data. Here, we propose to use scientific articles as a new milestone for text summarization: large-scale training data come almost for free with two types of high-quality summaries at different levels - the title and the abstract. We generate two novel multi-sentence summarization datasets from scientific articles and test the suitability of a wide range of existing extractive and abstractive neural network-based summarization approaches. Our analysis demonstrates that scientific papers are suitable for data-driven text summarization. Our results could serve as valuable benchmarks for scaling sequence-to-sequence models to very long sequences.

Connected through hyperedge HE-1 (induced by source paper "two huge title and keyword generation corpora of research articles"):
  with members:
    - "neural keyphrase generation via reinforcement learning with adaptive rewards"
    - "sentiment analysis of czech texts an algorithmic survey"
    - "a review of keyphrase extraction"
    - "efficiency metrics for data driven models a text summarization case study"
    - "keyphrase generation a multi aspect survey"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "attention is all you need"
    - "deep keyphrase generation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "samsum corpus a human annotated dialogue dataset for abstractive summarization"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "sample efficient text summarization using a single pre trained transformer"
    - "pretraining based natural language generation for text summarization"
    - "abstractive summarization of spoken and written conversation"
    - "abstractive dialogue summarization with sentence gated modeling optimized by dialogue acts"
    - "bottom up abstractive summarization"
    - "fast abstractive summarization with reinforce selected sentence rewriting"

Connected through hyperedge HE-4 (induced by source paper "summary refinement through denoising"):
  with members:
    - "bottom up abstractive summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "get to the point summarization with pointer generator networks"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"
    - "teaching machines to read and comprehend"

Connected through hyperedge HE-5 (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"
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "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-6 (induced by source paper "improving scientific article visibility by neural title simplification"):
  with members:
    - "opennmt neural machine translation toolkit"
    - "dynamic multi level multi task learning for sentence simplification"
    - "sentence simplification with deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "joint copying and restricted generation for paraphrase"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"

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)