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: "efficient adaptation of pretrained transformers for abstractive summarization"
  Abstract: Large-scale learning of transformer language models has yielded improvements on a variety of natural language understanding tasks. Whether they can be effectively adapted for summarization, however, has been less explored, as the learned representations are less seamlessly integrated into existing neural text production architectures. In this work, we propose two solutions for efficiently adapting pretrained transformer language models as text summarizers: source embeddings and domain-adaptive training. We test these solutions on three abstractive summarization datasets, achieving new state of the art performance on two of them. Finally, we show that these improvements are achieved by producing more focused summaries with fewer superfluous and that performance improvements are more pronounced on more abstractive datasets.

Connected through hyperedge HE-1 (induced by source paper "summary level training of sentence rewriting for abstractive summarization"):
  with members:
    - "fine tune bert for extractive summarization"
    - "bertscore evaluating text generation with bert"
    - "pretraining based natural language generation for text summarization"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "neural extractive text summarization with syntactic compression"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep communicating agents for abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "cooperative generator discriminator networks for abstractive summarization with narrative flow"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "sample efficient text summarization using a single pre trained transformer"
    - "bertscore evaluating text generation with bert"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "discourse aware neural rewards for coherent text generation"

Connected through hyperedge HE-3 (induced by source paper "abstractive dialog summarization with semantic scaffolds"):
  with members:
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "deep communicating agents for abstractive summarization"
    - "abstractive dialogue summarization with sentence gated modeling optimized by dialogue acts"
    - "bottom up abstractive summarization"
    - "dial2desc end to end dialogue description generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "classification betters regression in query based multi document summarisation techniques for question answering macquarie university at bioasq7b"):
  with members:
    - "macquarie university at bioasq 6b deep learning and deep reinforcement learning for query based multi document summarisation"
    - "supervised machine learning for extractive query based summarisation of biomedical data"
    - "neural latent extractive document summarization"
    - "get to the point summarization with pointer generator networks"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "a neural attention model for abstractive sentence summarization"

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)