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: "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.

Connected through hyperedge HE-1 (induced by source paper "a neural topical expansion framework for unstructured persona oriented dialogue generation"):
  with members:
    - "a pre training based personalized dialogue generation model with persona sparse data"
    - "an adversarial learning framework for a persona based multi turn dialogue model"
    - "refnet a reference aware network for background based conversation"
    - "thinking globally acting locally distantly supervised global to local knowledge selection for background based conversation"
    - "learning to speak and act in a fantasy text adventure game"
    - "personalized dialogue generation with diversified traits"
    - "learning personas from dialogue with attentive memory networks"
    - "training millions of personalized dialogue agents"

Connected through hyperedge HE-2 (induced by source paper "generating persona consistent dialogues by exploiting natural language inference"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "evaluating coherence in dialogue systems using entailment"
    - "a study of reinforcement learning for neural machine translation"
    - "dialogue natural language inference"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "deep reinforcement learning for chinese zero pronoun resolution"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantic sentence matching with densely connected recurrent and co attentive information"

Connected through hyperedge HE-3 (induced by source paper "utterance to utterance interactive matching network for multi turn response selection in retrieval based chatbots"):
  with members:
    - "dually interactive matching network for personalized response selection in retrieval based chatbots"
    - "interactive matching network for multi turn response selection in retrieval based chatbots"
    - "building sequential inference models for end to end response selection"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "end to end neural coreference resolution"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "bilateral multi perspective matching for natural language sentences"

Connected through hyperedge HE-4 (induced by source paper "a pre training based personalized dialogue generation model with persona sparse data"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "pretraining methods for dialog context representation learning"
    - "ernie enhanced representation through knowledge integration"
    - "challenges in building intelligent open domain dialog systems"
    - "sentilr linguistic knowledge enhanced language representation for sentiment analysis"
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "personalized dialogue generation with diversified traits"

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.AI(Artificial Intelligence)