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: "implicit deep latent variable models for text generation"
  Abstract: Deep latent variable models (LVM) such as variational auto-encoder (VAE) have recently played an important role in text generation. One key factor is the exploitation of smooth latent structures to guide the generation. However, the representation power of VAEs is limited due to two reasons: (1) the Gaussian assumption is often made on the variational posteriors; and meanwhile (2) a notorious "posterior collapse" issue occurs. In this paper, we advocate sample-based representations of variational distributions for natural language, leading to implicit latent features, which can provide flexible representation power compared with Gaussian-based posteriors. We further develop an LVM to directly match the aggregated posterior to the prior. It can be viewed as a natural extension of VAEs with a regularization of maximizing mutual information, mitigating the "posterior collapse" issue. We demonstrate the effectiveness and versatility of our models in various text generation scenarios, including language modeling, unaligned style transfer, and dialog response generation. The source code to reproduce our experimental results is available on GitHub.

Connected through hyperedge HE-1 (induced by source paper "sentencemim a latent variable language model"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "dynamic evaluation of transformer language models"
    - "preventing posterior collapse with delta vaes"
    - "a stable variational autoencoder for text modelling"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "improved variational autoencoders for text modeling using dilated convolutions"

Connected through hyperedge HE-2 (induced by source paper "sampling good latent variables via cpp vaes vaes with condition posterior as prior"):
  with members:
    - "a surprisingly effective fix for deep latent variable modeling of text"
    - "learning variations in human motion via mix and match perturbation"
    - "mixture content selection for diverse sequence generation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "learning trajectory dependencies for human motion prediction"
    - "mixture models for diverse machine translation tricks of the trade"
    - "diversity sensitive conditional generative adversarial networks"
    - "videoflow a flow based generative model for video"

Connected through hyperedge HE-3 (induced by source paper "plato pre trained dialogue generation model with discrete latent variable"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "generating multiple diverse responses with multi mapping and posterior mapping selection"
    - "know more about each other evolving dialogue strategy via compound assessment"
    - "ctrl a conditional transformer language model for controllable generation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "audio visual scene aware dialog"
    - "unified language model pre training for natural language understanding and generation"

Connected through hyperedge HE-4 (induced by source paper "preventing posterior collapse in sequence vaes with pooling"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "a hierarchical latent structure for variational conversation modeling"
    - "spherical latent spaces for stable variational autoencoders"
    - "toward controlled generation of text"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "supervised learning of universal sentence representations from natural language inference data"

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.LG(Machine Learning)