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 supervised approach to extractive summarisation of scientific papers"
  Abstract: Automatic summarisation is a popular approach to reduce a document to its main arguments. Recent research in the area has focused on neural approaches to summarisation, which can be very data-hungry. However, few large datasets exist and none for the traditionally popular domain of scientific publications, which opens up challenging research avenues centered on encoding large, complex documents. In this paper, we introduce a new dataset for summarisation of computer science publications by exploiting a large resource of author provided summaries and show straightforward ways of extending it further. We develop models on the dataset making use of both neural sentence encoding and traditionally used summarisation features and show that models which encode sentences as well as their local and global context perform best, significantly outperforming well-established baseline methods.

Connected through hyperedge HE-1 (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-2 (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"
    - "text summarization techniques a brief survey"
    - "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-3 (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"
    - "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"
    - "scientific article summarization using citation context and article s discourse structure"

Connected through hyperedge HE-4 (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"
    - "data driven summarization of scientific articles"
    - "scientific document summarization via citation contextualization and scientific discourse"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "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-5 (induced by source paper "billsum a corpus for automatic summarization of us legislation"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "party matters enhancing legislative embeddings with author attributes for vote prediction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how predictable is your state leveraging lexical and contextual information for predicting legislative floor action at the state level"
    - "teaching machines to read and comprehend"

Connected through hyperedge HE-6 (induced by source paper "data driven summarization of scientific articles"):
  with members:
    - "convolutional sequence to sequence learning"
    - "towards abstraction from extraction multiple timescale gated recurrent unit for summarization"
    - "cutting off redundant repeating generations for neural abstractive summarization"
    - "neural machine translation in linear time"
    - "fully character level neural machine translation without explicit segmentation"
    - "using centroids of word embeddings and word mover s distance for biomedical document retrieval in question answering"
    - "sequence to sequence rnns for text summarization"
    - "a neural attention model for abstractive sentence 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)