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: "quantized dialog language model for goal oriented conversational systems"
  Abstract: We propose a novel methodology to address dialog learning in the context of goal-oriented conversational systems. The key idea is to quantize the dialog space into clusters and create a language model across the clusters, thus allowing for an accurate choice of the next utterance in the conversation. The language model relies on n-grams associated with clusters of utterances. This quantized-dialog language model methodology has been applied to the end-to-end goal-oriented track of the latest Dialog System Technology Challenges (DSTC6). The objective is to find the correct system utterance from a pool of candidates in order to complete a dialog between a user and an automated restaurant-reservation system. Our results show that the technique proposed in this paper achieves high accuracy regarding selection of the correct candidate utterance, and outperforms other state-of-the-art approaches based on neural networks.

Connected through hyperedge HE-1 (induced by source paper "unsupervised dialog structure learning"):
  with members:
    - "zero shot dialog generation with cross domain latent actions"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "sentiment adaptive end to end dialog systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "discovering discrete latent topics with neural variational inference"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a network based end to end trainable task oriented dialogue system"
    - "language as a latent variable discrete generative models for sentence compression"

Connected through hyperedge HE-2 (induced by source paper "the rapidly changing landscape of conversational agents"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "toward continual learning for conversational agents"
    - "not all dialogues are created equal instance weighting for neural conversational models"
    - "a knowledge grounded neural conversation model"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "adversarial learning for neural dialogue generation"
    - "augmenting end to end dialog systems with commonsense knowledge"

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)