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 "learning sentiment memories for sentiment modification without parallel data"):
  Abstract: The task of sentiment modification requires reversing the sentiment of the input and preserving the sentiment-independent content. However, aligned sentences with the same content but different sentiments are usually unavailable. Due to the lack of such parallel data, it is hard to extract sentiment independent content and reverse the sentiment in an unsupervised way. Previous work usually can not reconcile sentiment transformation and content preservation. In this paper, motivated by the fact the non-emotional context (e.g., "staff") provides strong cues for the occurrence of emotional words (e.g., "friendly"), we propose a novel method that automatically extracts appropriate sentiment information from learned sentiment memories according to specific context. Experiments show that our method substantially improves the content preservation degree and achieves the state-of-the-art performance.

Includes members:
  Member "global encoding for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gret global representation enhanced transformer")
  Member "autoencoder as assistant supervisor improving text representation for chinese social media text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how to write summaries with patterns learning towards abstractive summarization through prototype editing")
  Member "unpaired sentiment to sentiment translation a cycled reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "style transfer from non parallel text by cross alignment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "structured attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep adaptive semantic logic dasl compiling declarative knowledge into deep neural networks")
  Member "style transfer in text exploration and evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "table to text generation by structure aware seq2seq learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic natural language generation using dependency information")
  Member "end to end memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning video object segmentation from unlabeled videos")

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)