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: "deepchannel salience estimation by contrastive learning for extractive document summarization"
  Abstract: We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization. Given any document-summary pair, we estimate a salience score, which is modeled using an attention-based deep neural network, to represent the salience degree of the summary for yielding the document. We devise a contrastive training strategy to learn the salience estimation network, and then use the learned salience score as a guide and iteratively extract the most salient sentences from the document as our generated summary. In experiments, our model not only achieves state-of-the-art ROUGE scores on CNN/Daily Mail dataset, but also shows strong robustness in the out-of-domain test on DUC2007 test set. Moreover, our model reaches a ROUGE-1 F-1 score of 39.41 on CNN/Daily Mail test set with merely $1 / 100$ training set, demonstrating a tremendous data efficiency.

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"
    - "efficient adaptation of pretrained transformers for abstractive 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"

Connected through hyperedge HE-2 (induced by source paper "discourse aware neural extractive model for text summarization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "fine tune bert for extractive summarization"
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "searching for effective neural extractive summarization what works and what s next"
    - "an entity driven framework for abstractive summarization"
    - "neural extractive text summarization with syntactic compression"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "deep communicating agents for abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "pretraining based natural language generation for text summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "deep contextualized word representations"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "language model pre training for hierarchical document representations"

Connected through hyperedge HE-4 (induced by source paper "attention optimization for abstractive document summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"
    - "discriminative feature learning for unsupervised video summarization"
    - "closed book training to improve summarization encoder memory"
    - "neural document summarization by jointly learning to score and select sentences"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "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)