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 "content selection in data to text systems a survey"):
  Abstract: Data-to-text systems are powerful in generating reports from data automatically and thus they simplify the presentation of complex data. Rather than presenting data using visualisation techniques, data-to-text systems use natural (human) language, which is the most common way for human-human communication. In addition, data-to-text systems can adapt their output content to users' preferences, background or interests and therefore they can be pleasant for users to interact with. Content selection is an important part of every data-to-text system, because it is the module that determines which from the available information should be conveyed to the user. This survey initially introduces the field of data-to-text generation, describes the general data-to-text system architecture and then it reviews the state-of-the-art content selection methods. Finally, it provides recommendations for choosing an approach and discusses opportunities for future research.

Includes members:
  Member "natural language generation enhances human decision making with uncertain information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explanation in human ai systems a literature meta review synopsis of key ideas and publications and bibliography for explainable ai")
  Member "linguistic descriptions for automatic generation of textual short term weather forecasts on real prediction data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploranative code quality documents")
  Member "individual and domain adaptation in sentence planning for dialogue":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards end to end learning for efficient dialogue agent by modeling looking ahead ability")
  Member "catching the drift probabilistic content models with applications to generation and summarization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastic natural language generation using dependency information")
  Member "acquiring correct knowledge for natural language generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "words of estimative correlation studying verbalizations of scatterplots")

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)