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: "neural text generation past present and beyond"
  Abstract: This paper presents a systematic survey on recent development of neural text generation models. Specifically, we start from recurrent neural network language models with the traditional maximum likelihood estimation training scheme and point out its shortcoming for text generation. We thus introduce the recently proposed methods for text generation based on reinforcement learning, re-parametrization tricks and generative adversarial nets (GAN) techniques. We compare different properties of these models and the corresponding techniques to handle their common problems such as gradient vanishing and generation diversity. Finally, we conduct a benchmarking experiment with different types of neural text generation models on two well-known datasets and discuss the empirical results along with the aforementioned model properties.

Connected through hyperedge HE-1 (induced by source paper "judge the judges a large scale evaluation study of neural language models for online review generation"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "on accurate evaluation of gans for language generation"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "texygen a benchmarking platform for text generation models"
    - "evaluating text gans as language models"
    - "unsupervised learning of sentence embeddings using compositional n gram features"

Connected through hyperedge HE-2 (induced by source paper "generalization in generation a closer look at exposure bias"):
  with members:
    - "deep reinforcement learning with distributional semantic rewards for abstractive summarization"
    - "quantifying exposure bias for neural language generation"
    - "sample efficient text summarization using a single pre trained transformer"
    - "autoregressive text generation beyond feedback loops"
    - "deep reinforcement learning for sequence to sequence models"
    - "learning actionable representations with goal conditioned policies"
    - "isolating sources of disentanglement in variational autoencoders"
    - "deep state space models for unconditional word generation"

Connected through hyperedge HE-3 (induced by source paper "semantic bottleneck scene generation"):
  with members:
    - "mirrorgan learning text to image generation by redescription"
    - "semantic image synthesis with spatially adaptive normalization"
    - "specifying object attributes and relations in interactive scene generation"
    - "high fidelity image generation with fewer labels"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "self supervised gans via auxiliary rotation loss"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-4 (induced by source paper "generating sentiment preserving fake online reviews using neural language models and their human and machine based detection"):
  with members:
    - "release strategies and the social impacts of language models"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"
    - "roberta a robustly optimized bert pretraining approach"
    - "large scale language modeling converging on 40gb of text in four hours"
    - "deep text classification can be fooled"
    - "simple fusion return of the language model"
    - "towards diverse text generation with inverse reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "a multi language platform for generating algebraic mathematical word problems"):
  with members:
    - "automatic conditional generation of personalized social media short texts"
    - "adversarial text generation via feature mover s distance"
    - "an analysis of neural language modeling at multiple scales"
    - "salsa text self attentive latent space based adversarial text generation"
    - "language gans falling short"
    - "on the state of the art of evaluation in neural language models"
    - "adversarial ranking for language generation"
    - "maximum likelihood augmented discrete generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "generative adversarial networks for text using word2vec intermediaries"):
  with members:
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "towards understanding linear word analogies"
    - "large scale gan training for high fidelity natural image synthesis"
    - "texygen a benchmarking platform for text generation models"
    - "adversarial generation of natural language"
    - "adversarial ranking for language generation"
    - "improved training of wasserstein gans"
    - "maximum likelihood augmented discrete generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "bfgan backward and forward generative adversarial networks for lexically constrained sentence generation"):
  with members:
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "texygen a benchmarking platform for text generation models"
    - "attention is all you need"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "get to the point summarization with pointer generator networks"
    - "adversarial learning for neural dialogue generation"
    - "affect lm a neural language model for customizable affective text generation"

Connected through hyperedge HE-8 (induced by source paper "on accurate evaluation of gans for language generation"):
  with members:
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "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"

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)