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: "multi turn dialogue response generation in an adversarial learning framework"
  Abstract: We propose an adversarial learning approach for generating multi-turn dialogue responses. Our proposed framework, hredGAN, is based on conditional generative adversarial networks (GANs). The GAN's generator is a modified hierarchical recurrent encoder-decoder network (HRED) and the discriminator is a word-level bidirectional RNN that shares context and word embeddings with the generator. During inference, noise samples conditioned on the dialogue history are used to perturb the generator's latent space to generate several possible responses. The final response is the one ranked best by the discriminator. The hredGAN shows improved performance over existing methods: (1) it generalizes better than networks trained using only the log-likelihood criterion, and (2) it generates longer, more informative and more diverse responses with high utterance and topic relevance even with limited training data. This improvement is demonstrated on the Movie triples and Ubuntu dialogue datasets using both automatic and human evaluations.

Connected through hyperedge HE-1 (induced by source paper "improving neural conversational models with entropy based data filtering"):
  with members:
    - "deep learning based chatbot models"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "neural response ranking for social conversation a data efficient approach"
    - "automatic evaluation of neural personality based chatbots"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "towards explainable and controllable open domain dialogue generation with dialogue acts"

Connected through hyperedge HE-2 (induced by source paper "dlgnet a transformer based model for dialogue response generation"):
  with members:
    - "an adversarial learning framework for a persona based multi turn dialogue model"
    - "the curious case of neural text degeneration"
    - "ernie enhanced representation through knowledge integration"
    - "adversarial bootstrapping for dialogue model training"
    - "defending against neural fake news"
    - "transformer xl attentive language models beyond a fixed length context"
    - "ernie 2 0 a continual pre training framework for language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-3 (induced by source paper "adversarial bootstrapping for dialogue model training"):
  with members:
    - "an adversarial learning framework for a persona based multi turn dialogue model"
    - "another diversity promoting objective function for neural dialogue generation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "attention is all you need"
    - "hierarchical recurrent attention network for response generation"
    - "adversarial learning for neural dialogue generation"
    - "a dual encoder sequence to sequence model for open domain dialogue modeling"

Connected through hyperedge HE-4 (induced by source paper "an adversarial learning framework for a persona based multi turn dialogue model"):
  with members:
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "hierarchical recurrent attention network for response generation"
    - "a persona based neural conversation model"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "a neural conversational model"
    - "a diversity promoting objective function for neural conversation models"

Connected through hyperedge HE-5 (induced by source paper "a persona based multi turn conversation model in an adversarial learning framework"):
  with members:
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "hierarchical recurrent attention network for response generation"
    - "a persona based neural conversation model"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "a neural conversational model"
    - "neural machine translation by jointly learning to align and translate"

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)