human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "selective attention encoders by syntactic graph convolutional networks for document summarization"):
  Abstract: Abstractive text summarization is a challenging task, and one need to design a mechanism to effectively extract salient information from the source text and then generate a summary. A parsing process of the source text contains critical syntactic or semantic structures, which is useful to generate more accurate summary. However, modeling a parsing tree for text summarization is not trivial due to its non-linear structure and it is harder to deal with a document that includes multiple sentences and their parsing trees. In this paper, we propose to use a graph to connect the parsing trees from the sentences in a document and utilize the stacked graph convolutional networks (GCNs) to learn the syntactic representation for a document. The selective attention mechanism is used to extract salient information in semantic and structural aspect and generate an abstractive summary. We evaluate our approach on the CNN/Daily Mail text summarization dataset. The experimental results show that the proposed GCNs based selective attention approach outperforms the baselines and achieves the state-of-the-art performance on the dataset.

Includes members:
  Member "graph convolution over pruned dependency trees improves relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "k adapter infusing knowledge into pre trained models with adapters")
  Member "a unified model for extractive and abstractive summarization using inconsistency loss":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")
  Member "bottom up abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers")
  Member "ranking sentences for extractive summarization with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning by semantic similarity makes abstractive summarization better")
  Member "structure infused copy mechanisms for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")
  Member "neural document summarization by jointly learning to score and select sentences":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "length controllable abstractive summarization by guiding with summary prototype")
  Member "fast abstractive summarization with reinforce selected sentence rewriting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")
  Member "selective encoding for abstractive sentence summarization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks")

Question: Please tell me which class the source paper 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)