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 "exploiting persona information for diverse generation of conversational responses"):
  Abstract: In human conversations, due to their personalities in mind, people can easily carry out and maintain the conversations. Giving conversational context with persona information to a chatbot, how to exploit the information to generate diverse and sustainable conversations is still a non-trivial task. Previous work on persona-based conversational models successfully make use of predefined persona information and have shown great promise in delivering more realistic responses. And they all learn with the assumption that given a source input, there is only one target response. However, in human conversations, there are massive appropriate responses to a given input message. In this paper, we propose a memory-augmented architecture to exploit persona information from context and incorporate a conditional variational autoencoder model together to generate diverse and sustainable conversations. We evaluate the proposed model on a benchmark persona-chat dataset. Both automatic and human evaluations show that our model can deliver more diverse and more engaging persona-based responses than baseline approaches.

Includes members:
  Member "deepcopy grounded response generation with hierarchical pointer networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attention over parameters for dialogue systems")
  Member "learning end to end goal oriented dialog with multiple answers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inset sentence infilling with inter sentential generative pre training")
  Member "from eliza to xiaoice challenges and opportunities with social chatbots":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low resource knowledge grounded dialogue generation")
  Member "personalizing dialogue agents i have a dog do you have pets too":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention over parameters for dialogue systems")
  Member "why are sequence to sequence models so dull understanding the low diversity problem of chatbots":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "do massively pretrained language models make better storytellers")
  Member "assigning personality identity to a chatting machine for coherent conversation generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions")
  Member "learning discourse level diversity for neural dialog models using conditional variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "non autoregressive neural dialogue generation")
  Member "a persona based neural conversation model":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "non autoregressive neural dialogue 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.AI(Artificial Intelligence)