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: "learning to write with cooperative discriminators"
  Abstract: Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models, but when used to generate natural language their output tends to be overly generic, repetitive, and self-contradictory. We postulate that the objective function optimized by RNN language models, which amounts to the overall perplexity of a text, is not expressive enough to capture the notion of communicative goals described by linguistic principles such as Grice's Maxims. We propose learning a mixture of multiple discriminative models that can be used to complement the RNN generator and guide the decoding process. Human evaluation demonstrates that text generated by our system is preferred over that of baselines by a large margin and significantly enhances the overall coherence, style, and information content of the generated text.

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 "analysis of diversity accuracy tradeoff in image captioning"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "the curious case of neural text degeneration"
    - "mixture models for diverse machine translation tricks of the trade"
    - "describing like humans on diversity in image captioning"
    - "hierarchical neural story generation"
    - "adversarial inference for multi sentence video description"
    - "attention is all you need"
    - "speaking the same language matching machine to human captions by adversarial training"

Connected through hyperedge HE-3 (induced by source paper "incremental sampling without replacement for sequence models"):
  with members:
    - "the curious case of neural text degeneration"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "frangel component based synthesis with control structures"
    - "spoc search based pseudocode to code"
    - "hierarchical neural story generation"
    - "attention is all you need"
    - "neural combinatorial optimization with reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "personalized dialogue generation with diversified traits"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"
    - "neural approaches to conversational ai"

Connected through hyperedge HE-5 (induced by source paper "training language gans from scratch"):
  with members:
    - "the curious case of neural text degeneration"
    - "on accurate evaluation of gans for language generation"
    - "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 "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"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "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-7 (induced by source paper "plug and play language models a simple approach to controlled text generation"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "simple and effective noisy channel modeling for neural machine translation"
    - "style transformer unpaired text style transfer without disentangled latent representation"
    - "the curious case of neural text degeneration"
    - "ctrl a conditional transformer language model for controllable generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fine tuning language models from human preferences"
    - "putting machine translation in context with the noisy channel model"

Connected through hyperedge HE-8 (induced by source paper "towards coherent and cohesive long form text generation"):
  with members:
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "unsupervised text style transfer using language models as discriminators"
    - "discourse aware neural rewards for coherent text generation"
    - "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)