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: "dialogwae multimodal response generation with conditional wasserstein auto encoder"
  Abstract: Variational autoencoders (VAEs) have shown a promise in data-driven conversation modeling. However, most VAE conversation models match the approximate posterior distribution over the latent variables to a simple prior such as standard normal distribution, thereby restricting the generated responses to a relatively simple (e.g., single-modal) scope. In this paper, we propose DialogWAE, a conditional Wasserstein autoencoder (WAE) specially designed for dialogue modeling. Unlike VAEs that impose a simple distribution over the latent variables, DialogWAE models the distribution of data by training a GAN within the latent variable space. Specifically, our model samples from the prior and posterior distributions over the latent variables by transforming context-dependent random noise using neural networks and minimizes the Wasserstein distance between the two distributions. We further develop a Gaussian mixture prior network to enrich the latent space. Experiments on two widely-used datasets show that DialogWAE outperforms the state-of-the-art approaches in generating more coherent, informative and diverse responses.

Connected through hyperedge HE-1 (induced by source paper "towards a human like open domain chatbot"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "better automatic evaluation of open domain dialogue systems with contextualized embeddings"
    - "the curious case of neural text degeneration"
    - "unifying human and statistical evaluation for natural language generation"
    - "human and automatic detection of generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "the evolved transformer"

Connected through hyperedge HE-2 (induced by source paper "variational hierarchical dialog autoencoder for dialogue state tracking data augmentation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "dialog state tracking with reinforced data augmentation"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "user modeling for task oriented dialogues"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "a hierarchical latent structure for variational conversation modeling"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "posterior gan towards informative and coherent response generation with posterior generative adversarial network"):
  with members:
    - "improving neural conversational models with entropy based data filtering"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "a study of reinforcement learning for neural machine translation"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "improving variational encoder decoders in dialogue generation"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "latent variable dialogue models and their diversity"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-4 (induced by source paper "learning from easy to complex adaptive multi curricula learning for neural dialogue generation"):
  with members:
    - "improving neural conversational models with entropy based data filtering"
    - "recosa detecting the relevant contexts with self attention for multi turn dialogue generation"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "competence based curriculum learning for neural machine translation"
    - "simple and effective curriculum pointer generator networks for reading comprehension over long narratives"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-5 (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"
    - "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"
    - "training millions of personalized dialogue agents"

Connected through hyperedge HE-6 (induced by source paper "mixture content selection for diverse sequence generation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "mixture models for diverse machine translation tricks of the trade"
    - "deep communicating agents for abstractive summarization"
    - "bottom up abstractive summarization"
    - "variational recurrent neural machine translation"
    - "a hierarchical latent structure for variational conversation modeling"
    - "hierarchical neural story generation"
    - "spherical latent spaces for stable variational autoencoders"

Connected through hyperedge HE-7 (induced by source paper "towards generating long and coherent text with multi level latent variable models"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "topic guided variational autoencoders for text generation"
    - "variational sequential labelers for semi supervised learning"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "learning to write with cooperative discriminators"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "a hierarchical latent structure for variational conversation modeling"

Connected through hyperedge HE-8 (induced by source paper "towards coherent and cohesive long form text generation"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "discourse aware neural rewards for coherent text generation"
    - "learning to write with cooperative discriminators"
    - "neural approaches to conversational ai"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "adversarial learning for neural dialogue generation"
    - "adversarial ranking for language generation"

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)