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: "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"
  Abstract: Neural sequence-to-sequence models are currently the dominant approach in several natural language processing tasks, but require large parallel corpora. We present a sequence-to-sequence-to-sequence autoencoder (SEQ^3), consisting of two chained encoder-decoder pairs, with words used as a sequence of discrete latent variables. We apply the proposed model to unsupervised abstractive sentence compression, where the first and last sequences are the input and reconstructed sentences, respectively, while the middle sequence is the compressed sentence. Constraining the length of the latent word sequences forces the model to distill important information from the input. A pretrained language model, acting as a prior over the latent sequences, encourages the compressed sentences to be human-readable. Continuous relaxations enable us to sample from categorical distributions, allowing gradient-based optimization, unlike alternatives that rely on reinforcement learning. The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "summae zero shot abstractive text summarization using length agnostic auto encoders"):
  with members:
    - "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"
    - "meansum a neural model for unsupervised multi document abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "make lead bias in your favor a simple and effective method for news summarization"):
  with members:
    - "importance of copying mechanism for news headline generation"
    - "passage re ranking with bert"
    - "on the variance of the adaptive learning rate and beyond"
    - "bottlesum unsupervised and self supervised sentence summarization using the information bottleneck principle"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"
    - "text summarization with pretrained encoders"
    - "unified language model pre training for natural language understanding and generation"
    - "content selection in deep learning models of summarization"

Connected through hyperedge HE-3 (induced by source paper "learning autocomplete systems as a communication game"):
  with members:
    - "pythia ai assisted code completion system"
    - "learning deep hidden nonlinear dynamics from aggregate data"
    - "unsupervised sentence compression using denoising auto encoders"
    - "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-4 (induced by source paper "ted a pretrained unsupervised summarization model with theme modeling and denoising"):
  with members:
    - "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"
    - "a simple theoretical model of importance for summarization"

Connected through hyperedge HE-5 (induced by source paper "unsupervised multi document opinion summarization as copycat review generation"):
  with members:
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"
    - "generating wikipedia by summarizing long sequences"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "meansum a neural model for unsupervised multi document abstractive summarization"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "using the output embedding to improve language models"

Connected through hyperedge HE-6 (induced by source paper "bottlesum unsupervised and self supervised sentence summarization using the information bottleneck principle"):
  with members:
    - "simple unsupervised summarization by contextual matching"
    - "learning representations by maximizing mutual information across views"
    - "learning to encode text as human readable summaries using generative adversarial networks"
    - "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-7 (induced by source paper "deleter leveraging bert to perform unsupervised successive text compression"):
  with members:
    - "human acceptability judgements for extractive sentence compression"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "fast k best sentence compression"

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)