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: "a hierarchical neural autoencoder for paragraphs and documents"
  Abstract: Natural language generation of coherent long texts like paragraphs or longer documents is a challenging problem for recurrent networks models. In this paper, we explore an important step toward this generation task: training an LSTM (Long-short term memory) auto-encoder to preserve and reconstruct multi-sentence paragraphs. We introduce an LSTM model that hierarchically builds an embedding for a paragraph from embeddings for sentences and words, then decodes this embedding to reconstruct the original paragraph. We evaluate the reconstructed paragraph using standard metrics like ROUGE and Entity Grid, showing that neural models are able to encode texts in a way that preserve syntactic, semantic, and discourse coherence. While only a first step toward generating coherent text units from neural models, our work has the potential to significantly impact natural language generation and summarization\footnote{Code for the three models described in this paper can be found at www.stanford.edu/~jiweil/ .

Connected through hyperedge HE-1 (induced by source paper "denoising based sequence to sequence pre training for text generation"):
  with members:
    - "pre trained language model representations for language generation"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "mass masked sequence to sequence pre training for language generation"
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "cross lingual language model pretraining"
    - "unified language model pre training for natural language understanding and generation"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-2 (induced by source paper "multi modal attention network learning for semantic source code retrieval"):
  with members:
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "multimodal dual attention memory for video story question answering"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "a deep reinforced model for abstractive summarization"
    - "dual attention networks for multimodal reasoning and matching"

Connected through hyperedge HE-3 (induced by source paper "curiosity driven reinforcement learning for diverse visual paragraph generation"):
  with members:
    - "exploration by random network distillation"
    - "diverse and coherent paragraph generation from images"
    - "large scale study of curiosity driven learning"
    - "context aware visual policy network for sequence level image captioning"
    - "coarse to fine annotation enrichment for semantic segmentation learning"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "how images inspire poems generating classical chinese poetry from images with memory networks"
    - "improved image captioning via policy gradient optimization of spider"

Connected through hyperedge HE-4 (induced by source paper "retrieval based goal oriented dialogue generation"):
  with members:
    - "scaling multi domain dialogue state tracking via query reformulation"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "on extractive and abstractive neural document summarization with transformer language models"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "towards universal dialogue state tracking"
    - "a strong baseline for question relevancy ranking"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "sequence to sequence learning for task oriented dialogue with dialogue state representation"

Connected through hyperedge HE-5 (induced by source paper "unsupervised discovery of multimodal links in multi image multi sentence documents"):
  with members:
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"
    - "quantifying the visual concreteness of words and topics in multimodal datasets"
    - "exploring the limits of weakly supervised pretraining"
    - "learning transferable architectures for scalable image recognition"
    - "weakly supervised learning of visual relations"
    - "segmentation from natural language expressions"

Connected through hyperedge HE-6 (induced by source paper "inset sentence infilling with inter sentential generative pre training"):
  with members:
    - "tigs an inference algorithm for text infilling with gradient search"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "linguistic versus latent relations for modeling coherent flow in paragraphs"
    - "the curious case of neural text degeneration"
    - "what makes a good story designing composite rewards for visual storytelling"
    - "spanbert improving pre training by representing and predicting spans"
    - "abductive reasoning as self supervision for common sense question answering"
    - "sentence bert sentence embeddings using siamese bert networks"

Connected through hyperedge HE-7 (induced by source paper "what makes a good story designing composite rewards for visual storytelling"):
  with members:
    - "uniter learning universal image text representations"
    - "learning end to end goal oriented dialog with multiple answers"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "plan and write towards better automatic storytelling"
    - "hybrid retrieval generation reinforced agent for medical image report generation"
    - "learning neural templates for text generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "no metrics are perfect adversarial reward learning for visual storytelling"

Connected through hyperedge HE-8 (induced by source paper "message passing attention networks for document understanding"):
  with members:
    - "dialogue act classification with context aware self attention"
    - "energy based self attentive learning of abstractive communities for spoken language understanding"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph convolutional networks for text classification"
    - "how powerful are graph neural networks"
    - "kernel graph convolutional neural networks"
    - "neural message passing for quantum chemistry"
    - "a structured self attentive sentence embedding"

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)