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: "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
  Abstract: Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly. Single document summarization (SDS) systems have benefited from advances in neural encoder-decoder model thanks to the availability of large datasets. However, multi-document summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples. In this paper, we introduce Multi-News, the first large-scale MDS news dataset. Additionally, we propose an end-to-end model which incorporates a traditional extractive summarization model with a standard SDS model and achieves competitive results on MDS datasets. We benchmark several methods on Multi-News and release our data and code in hope that this work will promote advances in summarization in the multi-document setting.

Connected through hyperedge HE-1 (induced by source paper "pegasus pre training with extracted gap sentences for abstractive summarization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "bigpatent a large scale dataset for abstractive and coherent summarization"
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "sample efficient text summarization using a single pre trained transformer"
    - "billsum a corpus for automatic summarization of us legislation"
    - "searching for effective neural extractive summarization what works and what s next"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"

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"
    - "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-3 (induced by source paper "using local knowledge graph construction to scale seq2seq models to multi document inputs"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformer xl attentive language models beyond a fixed length context"
    - "lattice cnns for matching based chinese question answering"
    - "adaptive attention span in transformers"
    - "eli5 long form question answering"
    - "graph neural networks a review of methods and applications"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "informative and controllable opinion summarization"):
  with members:
    - "hierarchical transformers for multi document summarization"
    - "generating summaries with topic templates and structured convolutional decoders"
    - "improving abstraction in text summarization"
    - "generating wikipedia by summarizing long sequences"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "controlling linguistic style aspects in neural language generation"
    - "get to the point summarization with pointer generator networks"

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)