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 neural single document summarization of reviews via learning latent discourse structure and its ranking"
  Abstract: This paper focuses on the end-to-end abstractive summarization of a single product review without supervision. We assume that a review can be described as a discourse tree, in which the summary is the root, and the child sentences explain their parent in detail. By recursively estimating a parent from its children, our model learns the latent discourse tree without an external parser and generates a concise summary. We also introduce an architecture that ranks the importance of each sentence on the tree to support summary generation focusing on the main review point. The experimental results demonstrate that our model is competitive with or outperforms other unsupervised approaches. In particular, for relatively long reviews, it achieves a competitive or better performance than supervised models. The induced tree shows that the child sentences provide additional information about their parent, and the generated summary abstracts the entire review.

Connected through hyperedge HE-1 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization"):
  with members:
    - "evaluating discourse in structured text representations"
    - "text summarization with pretrained encoders"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "toward abstractive summarization using semantic representations"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "structure infused copy mechanisms for abstractive summarization"
    - "a discourse aware attention model for abstractive summarization of long documents"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "abstractive document summarization without parallel data"):
  with members:
    - "unsupervised sentence compression using denoising auto encoders"
    - "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-4 (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"
    - "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"

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)