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: "inferring strategies for sentence ordering in multidocument news summarization"
  Abstract: The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocument summarization where summary sentences may be drawn from different input articles. In this paper, we propose a methodology for studying the properties of ordering information in the news genre and describe experiments done on a corpus of multiple acceptable orderings we developed for the task. Based on these experiments, we implemented a strategy for ordering information that combines constraints from chronological order of events and topical relatedness. Evaluation of our augmented algorithm shows a significant improvement of the ordering over two baseline strategies.

Connected through hyperedge HE-1 (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"
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "deep attentive ranking networks for learning to order sentences"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "context is key new approaches to neural coherence modeling"
    - "an efficient framework for learning sentence representations"
    - "attention is all you need"
    - "sentence ordering and coherence modeling using recurrent neural networks"
    - "sort story sorting jumbled images and captions into stories"
    - "neural sentence ordering"

Connected through hyperedge HE-3 (induced by source paper "graph based neural sentence ordering"):
  with members:
    - "semantic neural machine translation using amr"
    - "graph to sequence learning using gated graph neural networks"
    - "n ary relation extraction using graph state lstm"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "a graph to sequence model for amr to text generation"
    - "sentence state lstm for text representation"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "multi task learning for coherence modeling"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "coherence modeling of asynchronous conversations a neural entity grid approach"
    - "discourse coherence in the wild a dataset evaluation and methods"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "auxiliary objectives for neural error detection models"
    - "sentence ordering and coherence modeling using recurrent neural networks"

Connected through hyperedge HE-5 (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"
    - "applying natural language generation to indicative summarization"

Connected through hyperedge HE-6 (induced by source paper "specificity based sentence ordering for multi document extractive risk summarization"):
  with members:
    - "enriching word vectors with subword information"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "efficient estimation of word representations in vector space"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-7 (induced by source paper "learning sentence embeddings for coherence modelling and beyond"):
  with members:
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"
    - "get to the point summarization with pointer generator networks"
    - "text coherence analysis based on deep neural network"
    - "a deep reinforced model for abstractive summarization"
    - "sentence ordering and coherence modeling using recurrent neural networks"
    - "neural sentence ordering"
    - "bag of tricks for efficient text classification"
    - "end to end neural sentence ordering using pointer network"

Connected through hyperedge HE-8 (induced by source paper "context is key new approaches to neural coherence modeling"):
  with members:
    - "sentence ordering and coherence modeling using recurrent neural networks"
    - "neural sentence ordering"
    - "neural net models for open domain discourse coherence"
    - "catching the drift probabilistic content models with applications to generation and 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.AI(Artificial Intelligence)