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: "meansum a neural model for unsupervised multi document abstractive summarization"
  Abstract: Abstractive summarization has been studied using neural sequence transduction methods with datasets of large, paired document-summary examples. However, such datasets are rare and the models trained from them do not generalize to other domains. Recently, some progress has been made in learning sequence-to-sequence mappings with only unpaired examples. In our work, we consider the setting where there are only documents (product or business reviews) with no summaries provided, and propose an end-to-end, neural model architecture to perform unsupervised abstractive summarization. Our proposed model consists of an auto-encoder where the mean of the representations of the input reviews decodes to a reasonable summary-review while not relying on any review-specific features. We consider variants of the proposed architecture and perform an ablation study to show the importance of specific components. We show through automated metrics and human evaluation that the generated summaries are highly abstractive, fluent, relevant, and representative of the average sentiment of the input reviews. Finally, we collect a reference evaluation dataset and show that our model outperforms a strong extractive baseline.

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 "hierarchical transformers for multi document summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "structured neural summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "generating wikipedia by summarizing long sequences"
    - "towards dynamic computation graphs via sparse latent structure"
    - "bottom up abstractive summarization"
    - "automatic detection of vague words and sentences in privacy policies"

Connected through hyperedge HE-3 (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-4 (induced by source paper "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"):
  with members:
    - "a hierarchical end to end model for jointly improving text summarization and sentiment classification"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "structured attention networks"
    - "learning structured text representations"
    - "get to the point summarization with pointer generator networks"
    - "neural discourse structure for text categorization"
    - "a deep reinforced model for abstractive summarization"
    - "neural network based abstract generation for opinions and arguments"

Connected through hyperedge HE-5 (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"
    - "unsupervised sentence compression using denoising auto encoders"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "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-6 (induced by source paper "unsupervised multi document opinion summarization as copycat review generation"):
  with members:
    - "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"
    - "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"
    - "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-7 (induced by source paper "weakly supervised opinion summarization by leveraging external information"):
  with members:
    - "unsupervised multi document opinion summarization as copycat review generation"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural extractive summarization with side information"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

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)