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: "eval all trust a few do wrong to none comparing sentence generation models"
  Abstract: In this paper, we study recent neural generative models for text generation related to variational autoencoders. Previous works have employed various techniques to control the prior distribution of the latent codes in these models, which is important for sampling performance, but little attention has been paid to reconstruction error. In our study, we follow a rigorous evaluation protocol using a large set of previously used and novel automatic and human evaluation metrics, applied to both generated samples and reconstructions. We hope that it will become the new evaluation standard when comparing neural generative models for text.

Connected through hyperedge HE-1 (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"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "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-2 (induced by source paper "the curious case of neural text degeneration"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "neural text generation with unlikelihood training"
    - "on nmt search errors and model errors cat got your tongue"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "on accurate evaluation of gans for language generation"
    - "learning to write with cooperative discriminators"
    - "hierarchical neural story generation"
    - "texygen a benchmarking platform for text generation models"

Connected through hyperedge HE-3 (induced by source paper "latent code and text based generative adversarial networks for soft text generation"):
  with members:
    - "bilingual gan a step towards parallel text generation"
    - "spectral normalization for generative adversarial networks"
    - "texygen a benchmarking platform for text generation models"
    - "language gans falling short"
    - "generating continuous representations of medical texts"
    - "toward controlled generation of text"
    - "adversarial neural machine translation"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "educating text autoencoders latent representation guidance via denoising"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "universal transformers"
    - "multiple attribute text style transfer"
    - "content preserving text generation with attribute controls"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-5 (induced by source paper "on variational learning of controllable representations for text without supervision"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "generating sentences from disentangled syntactic and semantic spaces"
    - "preventing posterior collapse with delta vaes"
    - "transforming delete retrieve generate approach for controlled text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "content preserving text generation with attribute controls"
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"

Connected through hyperedge HE-6 (induced by source paper "deleter leveraging bert to perform unsupervised successive text compression"):
  with members:
    - "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"
    - "human acceptability judgements for extractive sentence compression"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "fast k best sentence compression"

Connected through hyperedge HE-7 (induced by source paper "on accurate evaluation of gans for language generation"):
  with members:
    - "neural text generation past present and beyond"
    - "universal sentence encoder"
    - "dp gan diversity promoting generative adversarial network for generating informative and diversified text"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "began boundary equilibrium generative adversarial networks"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "progressive growing of gans for improved quality stability and variation"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "language gans falling short"):
  with members:
    - "on accurate evaluation of gans for language generation"
    - "neural text generation past present and beyond"
    - "analyzing uncertainty in neural machine translation"
    - "adversarial text generation via feature mover s distance"
    - "towards diverse text generation with inverse reinforcement learning"
    - "cot cooperative training for generative modeling of discrete data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "texygen a benchmarking platform for text generation models"

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)