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: "discofuse a large scale dataset for discourse based sentence fusion"
  Abstract: Sentence fusion is the task of joining several independent sentences into a single coherent text. Current datasets for sentence fusion are small and insufficient for training modern neural models. In this paper, we propose a method for automatically-generating fusion examples from raw text and present DiscoFuse, a large scale dataset for discourse-based sentence fusion. We author a set of rules for identifying a diverse set of discourse phenomena in raw text, and decomposing the text into two independent sentences. We apply our approach on two document collections: Wikipedia and Sports articles, yielding 60 million fusion examples annotated with discourse information required to reconstruct the fused text. We develop a sequence-to-sequence model on DiscoFuse and thoroughly analyze its strengths and weaknesses with respect to the various discourse phenomena, using both automatic as well as human evaluation. Finally, we conduct transfer learning experiments with WebSplit, a recent dataset for text simplification. We show that pretraining on DiscoFuse substantially improves performance on WebSplit when viewed as a sentence fusion task.

Connected through hyperedge HE-1 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "the curious case of neural text degeneration"
    - "bertscore evaluating text generation with bert"
    - "on the use of bert for neural machine translation"
    - "how contextual are contextualized word representations comparing the geometry of bert elmo and gpt 2 embeddings"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "generalization in generation a closer look at exposure bias"
    - "moverscore text generation evaluating with contextualized embeddings and earth mover distance"

Connected through hyperedge HE-2 (induced by source paper "leveraging pre trained checkpoints for sequence generation tasks"):
  with members:
    - "fine tune bert for extractive summarization"
    - "bert rediscovers the classical nlp pipeline"
    - "sample efficient text summarization using a single pre trained transformer"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "mass masked sequence to sequence pre training for language generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "to tune or not to tune how about the best of both worlds"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-3 (induced by source paper "an entity driven framework for abstractive summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "entity commonsense representation for neural abstractive summarization"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "robust neural abstractive summarization systems and evaluation against adversarial information"

Connected through hyperedge HE-4 (induced by source paper "automatic fact guided sentence modification"):
  with members:
    - "paws paraphrase adversaries from word scrambling"
    - "bilinear attention networks"
    - "style transfer as unsupervised machine translation"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "allennlp a deep semantic natural language processing platform"
    - "fever a large scale dataset for fact extraction and verification"

Connected through hyperedge HE-5 (induced by source paper "encode tag realize high precision text editing"):
  with members:
    - "fine tune bert for extractive summarization"
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "imat unsupervised text attribute transfer via iterative matching and translation"
    - "step by step separating planning from realization in neural data to text generation"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "learning neural templates for text generation"
    - "ranking sentences for extractive summarization with reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "analyzing sentence fusion in abstractive summarization"):
  with members:
    - "hierarchical transformers for multi document summarization"
    - "what does bert look at an analysis of bert s attention"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "bottom up abstractive summarization"
    - "toward abstractive summarization using semantic representations"
    - "structure infused copy mechanisms for abstractive 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)