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 "generating summaries tailored to target characteristics"):
  Abstract: Recently, research efforts have gained pace to cater to varied user preferences while generating text summaries. While there have been attempts to incorporate a few handpicked characteristics such as length or entities, a holistic view around these preferences is missing and crucial insights on why certain characteristics should be incorporated in a specific manner are absent. With this objective, we provide a categorization around these characteristics relevant to the task of text summarization: one, focusing on what content needs to be generated and second, focusing on the stylistic aspects of the output summaries. We use our insights to provide guidelines on appropriate methods to incorporate various classes characteristics in sequence-to-sequence summarization framework. Our experiments with incorporating topics, readability and simplicity indicate the viability of the proposed prescriptions

Includes members:
  Member "multi task neural models for translating between styles within and across languages":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sequence to sequence pre training with data augmentation for sentence rewriting")
  Member "controlling personality based stylistic variation with neural natural language generators":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge")
  Member "style transfer through back translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "shaped shared private encoder decoder for text style adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "domain adaptive text style transfer")
  Member "unpaired sentiment to sentiment translation a cycled reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "toward controlled generation of text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust explanations for visual question answering")
  Member "style transfer from non parallel text by cross alignment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "controlling linguistic style aspects in neural language generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "variational template machine for data to text generation")

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)