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 "generative deep neural networks for dialogue a short review"):
  Abstract: Researchers have recently started investigating deep neural networks for dialogue applications. In particular, generative sequence-to-sequence (Seq2Seq) models have shown promising results for unstructured tasks, such as word-level dialogue response generation. The hope is that such models will be able to leverage massive amounts of data to learn meaningful natural language representations and response generation strategies, while requiring a minimum amount of domain knowledge and hand-crafting. An important challenge is to develop models that can effectively incorporate dialogue context and generate meaningful and diverse responses. In support of this goal, we review recently proposed models based on generative encoder-decoder neural network architectures, and show that these models have better ability to incorporate long-term dialogue history, to model uncertainty and ambiguity in dialogue, and to generate responses with high-level compositional structure.

Includes members:
  Member "deep reinforcement learning for dialogue generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "a network based end to end trainable task oriented dialogue system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "multiresolution recurrent neural networks an application to dialogue response generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "avgout a simple output probability measure to eliminate dull responses")
  Member "smart reply automated response suggestion for email":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fundamentals of recurrent neural network rnn and long short term memory lstm network")
  Member "dialog based language learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pseudo labeling and negative feedback learning for large scale multi label domain classification")
  Member "learning end to end goal oriented dialog":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "a hierarchical latent variable encoder decoder model for generating dialogues":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attention over parameters for dialogue systems")
  Member "building end to end dialogue systems using generative hierarchical neural network models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention over parameters for dialogue systems")

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)