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: "centroid based summarization of multiple documents sentence extraction utility based evaluation and user studies"
  Abstract: We present a multi-document summarizer, called MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We also describe two new techniques, based on sentence utility and subsumption, which we have applied to the evaluation of both single and multiple document summaries. Finally, we describe two user studies that test our models of multi-document summarization.

Connected through hyperedge HE-1 (induced by source paper "multi news a large scale multi document summarization dataset and abstractive hierarchical model"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "generating wikipedia by summarizing long sequences"
    - "bottom up abstractive summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "adapting the neural encoder decoder framework from single to multi document summarization"

Connected through hyperedge HE-2 (induced by source paper "sentence centrality revisited for unsupervised summarization"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "automatic text summarization what has been done and what has to be done"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "learning based single document summarization with compression and anaphoricity constraints"
    - "a neural attention model for abstractive sentence summarization"
    - "extraction of salient sentences from labelled documents"
    - "on using very large target vocabulary for neural machine translation"
    - "bayesian query focused summarization"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "inferring strategies for sentence ordering in multidocument news summarization"

Connected through hyperedge HE-4 (induced by source paper "extractive summarization via weighted dissimilarity and importance aligned key iterative algorithm"):
  with members:
    - "distributed representations of words and phrases and their compositionality"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-5 (induced by source paper "towards a neural network approach to abstractive multi document summarization"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "a neural attention model for abstractive sentence summarization"
    - "a hierarchical neural autoencoder for paragraphs and documents"

Connected through hyperedge HE-6 (induced by source paper "indosum a new benchmark dataset for indonesian text summarization"):
  with members:
    - "get to the point summarization with pointer generator networks"
    - "neural extractive summarization with side information"
    - "learning to select data for transfer learning with bayesian optimization"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "enriching word vectors with subword information"
    - "neural summarization by extracting sentences and words"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"

Connected through hyperedge HE-7 (induced by source paper "automated text summarisation and evidence based medicine a survey of two domains"):
  with members:
    - "summarization from medical documents a survey"
    - "ranking suspected answers to natural language questions using predictive annotation"

Connected through hyperedge HE-8 (induced by source paper "automatic argumentative zoning using word2vec"):
  with members:
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"

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)