human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "applying natural language generation to indicative summarization"
  Abstract: The task of creating indicative summaries that help a searcher decide whether to read a particular document is a difficult task. This paper examines the indicative summarization task from a generation perspective, by first analyzing its required content via published guidelines and corpus analysis. We show how these summaries can be factored into a set of document features, and how an implemented content planner uses the topicality document feature to create indicative multidocument query-based summaries.

Connected through hyperedge HE-1 (induced by source paper "automatic text summarization what has been done and what has to be done"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "learning based single document summarization with compression and anaphoricity constraints"
    - "a neural attention model for abstractive sentence summarization"
    - "extraction of salient sentences from labelled documents"
    - "on using very large target vocabulary for neural machine translation"
    - "bayesian query focused summarization"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "inferring strategies for sentence ordering in multidocument news summarization"

Connected through hyperedge HE-2 (induced by source paper "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"):
  with members:
    - "referenceless quality estimation for natural language generation"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "the e2e dataset new challenges for end to end generation"
    - "investigation of language understanding impact for reinforcement learning based dialogue systems"
    - "deep reinforcement learning for multi domain dialogue systems"
    - "natural language generation in dialogue using lexicalized and delexicalized data"

Connected through hyperedge HE-3 (induced by source paper "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"):
  with members:
    - "referenceless quality estimation for natural language generation"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "the e2e dataset new challenges for end to end generation"
    - "investigation of language understanding impact for reinforcement learning based dialogue systems"
    - "deep reinforcement learning for multi domain dialogue systems"
    - "natural language generation in dialogue using lexicalized and delexicalized data"

Question: Please tell me which class the center node 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)