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: "on accurate evaluation of gans for language generation"
  Abstract: Generative Adversarial Networks (GANs) are a promising approach to language generation. The latest works introducing novel GAN models for language generation use n-gram based metrics for evaluation and only report single scores of the best run. In this paper, we argue that this often misrepresents the true picture and does not tell the full story, as GAN models can be extremely sensitive to the random initialization and small deviations from the best hyperparameter choice. In particular, we demonstrate that the previously used BLEU score is not sensitive to semantic deterioration of generated texts and propose alternative metrics that better capture the quality and diversity of the generated samples. We also conduct a set of experiments comparing a number of GAN models for text with a conventional Language Model (LM) and find that neither of the considered models performs convincingly better than the LM.

Connected through hyperedge HE-1 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "the curious case of neural text degeneration"
    - "bertscore evaluating text generation with bert"
    - "on the use of bert for neural machine translation"
    - "how contextual are contextualized word representations comparing the geometry of bert elmo and gpt 2 embeddings"
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "generalization in generation a closer look at exposure bias"

Connected through hyperedge HE-2 (induced by source paper "multi object tracking via end to end tracklet searching and ranking"):
  with members:
    - "deep learning in video multi object tracking a survey"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "tracklet association tracker an end to end learning based association approach for multi object tracking"
    - "evaluating text gans as language models"
    - "unsupervised person re identification by deep learning tracklet association"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "style example guided text generation using generative adversarial transformers"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "real or fake learning to discriminate machine from human generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "pretraining based natural language generation for text summarization"
    - "multi task deep neural networks for natural language understanding"
    - "domain adaptive text style transfer"
    - "defending against neural fake news"

Connected through hyperedge HE-4 (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"
    - "neural text generation past present and beyond"
    - "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-5 (induced by source paper "training language gans from scratch"):
  with members:
    - "the curious case of neural text degeneration"
    - "learning to write with cooperative discriminators"
    - "the relativistic discriminator a key element missing from standard gan"
    - "spectral normalization for generative adversarial networks"
    - "universal sentence encoder"
    - "large scale gan training for high fidelity natural image synthesis"
    - "texygen a benchmarking platform for text generation models"
    - "evaluating text gans as language models"

Connected through hyperedge HE-6 (induced by source paper "autoregressive text generation beyond feedback loops"):
  with members:
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "large scale classification of structured objects using a crf with deep class embedding"
    - "deep state space models for unconditional word generation"
    - "language gans falling short"
    - "dp gan diversity promoting generative adversarial network for generating informative and diversified text"
    - "structured attention networks"
    - "advances in variational inference"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "araml a stable adversarial training framework for text generation"):
  with members:
    - "incorporating discriminator in sentence generation a gibbs sampling method"
    - "towards diverse text generation with inverse reinforcement learning"
    - "texygen a benchmarking platform for text generation models"
    - "cgmh constrained sentence generation by metropolis hastings sampling"
    - "language gans falling short"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "ruber an unsupervised method for automatic evaluation of open domain dialog systems"

Connected through hyperedge HE-8 (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"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "learning to write with cooperative discriminators"
    - "hierarchical neural story generation"
    - "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)