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 encode text as human readable summaries using generative adversarial networks"
  Abstract: Auto-encoders compress input data into a latent-space representation and reconstruct the original data from the representation. This latent representation is not easily interpreted by humans. In this paper, we propose training an auto-encoder that encodes input text into human-readable sentences, and unpaired abstractive summarization is thereby achieved. The auto-encoder is composed of a generator and a reconstructor. The generator encodes the input text into a shorter word sequence, and the reconstructor recovers the generator input from the generator output. To make the generator output human-readable, a discriminator restricts the output of the generator to resemble human-written sentences. By taking the generator output as the summary of the input text, abstractive summarization is achieved without document-summary pairs as training data. Promising results are shown on both English and Chinese corpora.

Connected through hyperedge HE-1 (induced by source paper "summae zero shot abstractive text summarization using length agnostic auto encoders"):
  with members:
    - "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "mass masked sequence to sequence pre training for language generation"
    - "ernie enhanced language representation with informative entities"
    - "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "neural text summarization a critical evaluation"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"):
  with members:
    - "selective encoding for abstractive sentence summarization"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "unsupervised neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "differentiable scheduled sampling for credit assignment"
    - "memory augmented neural networks with wormhole connections"
    - "controllable abstractive summarization"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning based text style transfer without parallel training corpus"):
  with members:
    - "improving reinforcement learning based image captioning with natural language prior"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "a study of reinforcement learning for neural machine translation"
    - "style transfer through back translation"
    - "shaped shared private encoder decoder for text style adaptation"
    - "polite dialogue generation without parallel data"
    - "graph2seq graph to sequence learning with attention based neural networks"

Connected through hyperedge HE-4 (induced by source paper "ted a pretrained unsupervised summarization model with theme modeling and denoising"):
  with members:
    - "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"
    - "make lead bias in your favor a simple and effective method for news summarization"
    - "on the variance of the adaptive learning rate and beyond"
    - "sentence centrality revisited for unsupervised summarization"
    - "text summarization with pretrained encoders"
    - "unsupervised text style transfer using language models as discriminators"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "unsupervised sentence compression using denoising auto encoders"

Connected through hyperedge HE-5 (induced by source paper "explicit sentence compression for neural machine translation"):
  with members:
    - "exploiting sentential context for neural machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "exploiting deep representations for neural machine translation"
    - "unsupervised sentence compression using denoising auto encoders"
    - "modeling localness for self attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-6 (induced by source paper "unsupervised text summarization via mixed model back translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "mass masked sequence to sequence pre training for language generation"
    - "bottom up abstractive summarization"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "get to the point summarization with pointer generator networks"
    - "style transfer in text exploration and evaluation"

Connected through hyperedge HE-7 (induced by source paper "bottlesum unsupervised and self supervised sentence summarization using the information bottleneck principle"):
  with members:
    - "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"
    - "simple unsupervised summarization by contextual matching"
    - "learning representations by maximizing mutual information across views"
    - "representation learning with contrastive predictive coding"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "unsupervised neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "abstractive text summarization using sequence to sequence rnns and beyond"

Connected through hyperedge HE-8 (induced by source paper "simple unsupervised summarization by contextual matching"):
  with members:
    - "unsupervised sentence compression using denoising auto encoders"
    - "unsupervised abstractive sentence summarization using length controlled variational autoencoder"
    - "deep contextualized word representations"
    - "online and linear time attention by enforcing monotonic alignments"
    - "a deep reinforced model for abstractive summarization"
    - "online segment to segment neural transduction"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "abstractive text summarization using sequence to sequence rnns and beyond"

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)