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: "personalizing a dialogue system with transfer reinforcement learning"
  Abstract: It is difficult to train a personalized task-oriented dialogue system because the data collected from each individual is often insufficient. Personalized dialogue systems trained on a small dataset can overfit and make it difficult to adapt to different user needs. One way to solve this problem is to consider a collection of multiple users' data as a source domain and an individual user's data as a target domain, and to perform a transfer learning from the source to the target domain. By following this idea, we propose "PETAL"(PErsonalized Task-oriented diALogue), a transfer-learning framework based on POMDP to learn a personalized dialogue system. The system first learns common dialogue knowledge from the source domain and then adapts this knowledge to the target user. This framework can avoid the negative transfer problem by considering differences between source and target users. The policy in the personalized POMDP can learn to choose different actions appropriately for different users. Experimental results on a real-world coffee-shopping data and simulation data show that our personalized dialogue system can choose different optimal actions for different users, and thus effectively improve the dialogue quality under the personalized setting.

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"
    - "exploiting persona information for diverse generation of conversational responses"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "ai powered text generation for harmonious human machine interaction current state and future directions"):
  with members:
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "learning personalized end to end goal oriented dialog"
    - "multi reward reinforced summarization with saliency and entailment"
    - "learning to generate reviews and discovering sentiment"
    - "key value retrieval networks for task oriented dialogue"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "assigning personality identity to a chatting machine for coherent conversation generation"
    - "automatic generation of natural language explanations"

Connected through hyperedge HE-3 (induced by source paper "domain adaptive dialog generation via meta learning"):
  with members:
    - "few shot generalization across dialogue tasks"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning for low resource neural machine translation"
    - "sentiment adaptive end to end dialog systems"
    - "adversarial domain adaptation for variational neural language generation in dialogue systems"
    - "prototypical networks for few shot learning"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-4 (induced by source paper "mala cross domain dialogue generation with action learning"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "carl aggregated search with context aware module embedding learning"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "zero shot dialog generation with cross domain latent actions"
    - "unsupervised text style transfer using language models as discriminators"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"

Connected through hyperedge HE-5 (induced by source paper "faster and safer training by embedding high level knowledge into deep reinforcement learning"):
  with members:
    - "leveraging human guidance for deep reinforcement learning tasks"
    - "recent advances in imitation learning from observation"
    - "hierarchical imitation and reinforcement learning"
    - "parameter transfer unit for deep neural networks"
    - "interactive learning from policy dependent human feedback"
    - "constrained policy optimization"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "safe reinforcement learning via shielding"

Connected through hyperedge HE-6 (induced by source paper "listening between the lines learning personal attributes from conversations"):
  with members:
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "deep contextualized word representations"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "a knowledge grounded neural conversation model"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "hierarchical recurrent attention network for response generation"

Connected through hyperedge HE-7 (induced by source paper "proactive human machine conversation with explicit conversation goals"):
  with members:
    - "towards deep conversational recommendations"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "towards exploiting background knowledge for building conversation systems"
    - "a knowledge grounded neural conversation model"
    - "flexible end to end dialogue system for knowledge grounded conversation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

Connected through hyperedge HE-8 (induced by source paper "generating multiple diverse responses for short text conversation"):
  with members:
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "latent intention dialogue models"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "adversarial learning for neural dialogue generation"
    - "a conditional variational framework for dialog generation"
    - "paraphrase generation with deep reinforcement learning"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

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)