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: "unsupervised sentence compression using denoising auto encoders"
  Abstract: In sentence compression, the task of shortening sentences while retaining the original meaning, models tend to be trained on large corpora containing pairs of verbose and compressed sentences. To remove the need for paired corpora, we emulate a summarization task and add noise to extend sentences and train a denoising auto-encoder to recover the original, constructing an end-to-end training regime without the need for any examples of compressed sentences. We conduct a human evaluation of our model on a standard text summarization dataset and show that it performs comparably to a supervised baseline based on grammatical correctness and retention of meaning. Despite being exposed to no target data, our unsupervised models learn to generate imperfect but reasonably readable sentence summaries. Although we underperform supervised models based on ROUGE scores, our models are competitive with a supervised baseline based on human evaluation for grammatical correctness and retention of meaning.

Connected through hyperedge HE-1 (induced by source paper "learning autocomplete systems as a communication game"):
  with members:
    - "pythia ai assisted code completion system"
    - "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"
    - "learning deep hidden nonlinear dynamics from aggregate data"
    - "planning inference and pragmatics in sequential language games"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "generating sentences by editing prototypes"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-2 (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"
    - "a simple theoretical model of importance for summarization"

Connected through hyperedge HE-3 (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"
    - "modeling localness for self attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to encode text as human readable summaries using generative adversarial networks"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-4 (induced by source paper "abstractive document summarization without parallel data"):
  with members:
    - "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "meansum a neural model for unsupervised multi document abstractive summarization"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "phrase based neural unsupervised machine translation"
    - "understanding back translation at scale"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-5 (induced by source paper "fiction sentence expansion and enhancement via focused objective and novelty curve sampling"):
  with members:
    - "incorporating discriminator in sentence generation a gibbs sampling method"
    - "on accurate evaluation of gans for language generation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "style transfer through back translation"
    - "hierarchical neural story generation"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "steering output style and topic in neural response generation"

Connected through hyperedge HE-6 (induced by source paper "controllable sentence simplification"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "reference less quality estimation of text simplification systems"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "easse easier automatic sentence simplification evaluation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "bleu is not suitable for the evaluation of text simplification"
    - "sentence simplification with memory augmented neural networks"
    - "dynamic multi level multi task learning for sentence simplification"

Connected through hyperedge HE-7 (induced by source paper "simple unsupervised summarization by contextual matching"):
  with members:
    - "unsupervised abstractive sentence summarization using length controlled variational autoencoder"
    - "deep contextualized word representations"
    - "learning to encode text as human readable summaries using generative adversarial networks"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "scalable micro planned generation of discourse from structured data"):
  with members:
    - "discourse aware neural rewards for coherent text generation"
    - "table to text describing table region with natural language"
    - "fever a large scale dataset for fact extraction and verification"
    - "split and rephrase"
    - "opennmt open source toolkit for neural machine translation"
    - "a neural knowledge language model"
    - "challenges in data to document generation"
    - "the e2e dataset new challenges for end to end 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)