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: "deep communicating agents for abstractive summarization"
  Abstract: We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. With deep communicating agents, the task of encoding a long text is divided across multiple collaborating agents, each in charge of a subsection of the input text. These encoders are connected to a single decoder, trained end-to-end using reinforcement learning to generate a focused and coherent summary. Empirical results demonstrate that multiple communicating encoders lead to a higher quality summary compared to several strong baselines, including those based on a single encoder or multiple non-communicating encoders.

Connected through hyperedge HE-1 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks"):
  with members:
    - "fine tune bert for extractive summarization"
    - "jointly extracting and compressing documents with summary state representations"
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "mass masked sequence to sequence pre training for language generation"
    - "generating summaries with topic templates and structured convolutional decoders"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "unified language model pre training for natural language understanding and generation"
    - "improving abstraction in text summarization"

Connected through hyperedge HE-2 (induced by source paper "unsupervised common question generation from multiple documents using reinforced contrastive coordinator"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "generating a common question from multiple documents using multi source encoder decoder models"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "asking clarifying questions in open domain information seeking conversations"
    - "a deep cascade model for multi document reading comprehension"
    - "towards coherent and cohesive long form text generation"
    - "multi style generative reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "joint parsing and generation for abstractive summarization"):
  with members:
    - "analyzing sentence fusion in abstractive summarization"
    - "controlling the amount of verbatim copying in abstractive summarization"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "generating wikipedia by summarizing long sequences"
    - "bottom up abstractive summarization"
    - "a tree based decoder for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "mixture content selection for diverse sequence generation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "mixture models for diverse machine translation tricks of the trade"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "bottom up abstractive summarization"
    - "variational recurrent neural machine translation"
    - "a hierarchical latent structure for variational conversation modeling"
    - "hierarchical neural story generation"
    - "spherical latent spaces for stable variational autoencoders"

Connected through hyperedge HE-5 (induced by source paper "pay less attention with lightweight and dynamic convolutions"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "self attention with relative position representations"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "scaling neural machine translation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "smoothed dilated convolutions for improved dense prediction"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-6 (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-7 (induced by source paper "expressing visual relationships via language"):
  with members:
    - "learning to globally edit images with textual description"
    - "discriminability objective for training descriptive captions"
    - "learning to describe differences between pairs of similar images"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "learning to color from language"
    - "pragmatically informative image captioning with character level reference"
    - "reinforced video captioning with entailment rewards"
    - "improved image captioning via policy gradient optimization of spider"

Connected through hyperedge HE-8 (induced by source paper "improving the similarity measure of determinantal point processes for extractive multi document summarization"):
  with members:
    - "parabank monolingual bitext generation and sentential paraphrasing via lexically constrained neural machine translation"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "improving abstraction in text summarization"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "soft layer specific multi task summarization with entailment and question generation"

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)