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 "select and attend towards controllable content selection in text generation"):
  Abstract: Many text generation tasks naturally contain two steps: content selection and surface realization. Current neural encoder-decoder models conflate both steps into a black-box architecture. As a result, the content to be described in the text cannot be explicitly controlled. This paper tackles this problem by decoupling content selection from the decoder. The decoupled content selection is human interpretable, whose value can be manually manipulated to control the content of generated text. The model can be trained end-to-end without human annotations by maximizing a lower bound of the marginal likelihood. We further propose an effective way to trade-off between performance and controllability with a single adjustable hyperparameter. In both data-to-text and headline generation tasks, our model achieves promising results, paving the way for controllable content selection in text generation.

Includes members:
  Member "toward unsupervised text content manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "controllable paraphrase generation with a syntactic exemplar")
  Member "towards content transfer through grounded text generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mask based unsupervised content transfer")
  Member "step by step separating planning from realization in neural data to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "paraphrase generation with latent bag of words")
  Member "global encoding for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gret global representation enhanced transformer")
  Member "a unified model for extractive and abstractive summarization using inconsistency loss":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")
  Member "reinforced self attention network a hybrid of hard and soft attention for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi level head wise match and aggregation in transformer for textual sequence matching")
  Member "bottom up abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers")
  Member "show control and tell a framework for generating controllable and grounded captions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "say as you wish fine grained control of image caption generation with abstract scene graphs")

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.CL(Computation and Language)