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 a practical guide"
  Abstract: Deep learning methods have recently achieved great empirical success on machine translation, dialogue response generation, summarization, and other text generation tasks. At a high level, the technique has been to train end-to-end neural network models consisting of an encoder model to produce a hidden representation of the source text, followed by a decoder model to generate the target. While such models have significantly fewer pieces than earlier systems, significant tuning is still required to achieve good performance. For text generation models in particular, the decoder can behave in undesired ways, such as by generating truncated or repetitive outputs, outputting bland and generic responses, or in some cases producing ungrammatical gibberish. This paper is intended as a practical guide for resolving such undesired behavior in text generation models, with the aim of helping enable real-world applications.

Connected through hyperedge HE-1 (induced by source paper "clinically accurate chest x ray report generation"):
  with members:
    - "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"
    - "mimic cxr jpg a large publicly available database of labeled chest radiographs"
    - "on the automatic generation of medical imaging reports"
    - "large scale automated reading of frontal and lateral chest x rays using dual convolutional neural networks"
    - "producing radiologist quality reports for interpretable artificial intelligence"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "learning to summarize radiology findings"
    - "unsupervised multimodal representation learning across medical images and reports"

Connected through hyperedge HE-2 (induced by source paper "keeping notes conditional natural language generation with a scratchpad mechanism"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "neural argument generation augmented with externally retrieved evidence"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "learning to ask neural question generation for reading comprehension"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "paperrobot incremental draft generation of scientific ideas"):
  with members:
    - "sql to text generation with graph to sequence model"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "learning to generate wikipedia summaries for underserved languages from wikidata"
    - "describing a knowledge base"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "hierarchical neural story generation"
    - "entity aware image caption generation"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-4 (induced by source paper "beta dvbf learning state space models for control from high dimensional observations"):
  with members:
    - "stochastic adversarial video prediction"
    - "stochastic video generation with a learned prior"
    - "time agnostic prediction predicting predictable video frames"
    - "a recurrent latent variable model for sequential data"
    - "embed to control a locally linear latent dynamics model for control from raw images"

Connected through hyperedge HE-5 (induced by source paper "guess who multilingual approach for the automated generation of author stylized poetry"):
  with members:
    - "what is wrong with style transfer for texts"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"
    - "generating sentences by editing prototypes"
    - "flexible and creative chinese poetry generation using neural memory"
    - "style transfer from non parallel text by cross alignment"
    - "controlling linguistic style aspects in neural language generation"

Connected through hyperedge HE-6 (induced by source paper "generating text through adversarial training using skip thought vectors"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "adversarial generation of natural language"
    - "improved training of wasserstein gans"
    - "recent trends in deep learning based natural language processing"
    - "long text generation via adversarial training with leaked information"
    - "text generation based on generative adversarial nets with latent variable"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "unrolled generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "neural text generation past present and beyond"):
  with members:
    - "texygen a benchmarking platform for text generation models"
    - "dp gan diversity promoting generative adversarial network for generating informative and diversified text"
    - "toward controlled generation of text"
    - "adversarial generation of natural language"
    - "adversarial multi task learning for text classification"
    - "maximum likelihood augmented discrete generative adversarial networks"
    - "long text generation via adversarial training with leaked information"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-8 (induced by source paper "how reasonable are common sense reasoning tasks a case study on the winograd schema challenge and swag"):
  with members:
    - "socialiqa commonsense reasoning about social interactions"
    - "exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge"
    - "annotation artifacts in natural language inference data"
    - "a simple method for commonsense reasoning"
    - "the natural language decathlon multitask learning as question answering"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "a knowledge hunting framework for common sense reasoning"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

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)