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: "bart denoising sequence to sequence pre training for natural language generation translation and comprehension"
  Abstract: We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also report ablation experiments that replicate other pretraining schemes within the BART framework, to better measure which factors most influence end-task performance.

Connected through hyperedge HE-1 (induced by source paper "a survey on contextual embeddings"):
  with members:
    - "episodic memory in lifelong language learning"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "visualizing and measuring the geometry of bert"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "emerging cross lingual structure in pretrained language models"
    - "bert rediscovers the classical nlp pipeline"
    - "unsupervised cross lingual representation learning at scale"
    - "q bert hessian based ultra low precision quantization of bert"

Connected through hyperedge HE-2 (induced by source paper "multilingual denoising pre training for neural machine translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "unsupervised cross lingual representation learning at scale"
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "pre trained language model representations for language generation"
    - "ctrl a conditional transformer language model for controllable generation"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-3 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "knowledge distillation from internal representations"
    - "small and practical bert models for sequence labeling"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "cloze driven pretraining of self attention networks"

Connected through hyperedge HE-4 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "pre trained language model representations for language generation"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "defending against neural fake news"
    - "albert a lite bert for self supervised learning of language representations"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"

Connected through hyperedge HE-5 (induced by source paper "univilm a unified video and language pre training model for multimodal understanding and generation"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "learning video representations using contrastive bidirectional transformer"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "mass masked sequence to sequence pre training for language generation"
    - "a case study on combining asr and visual features for generating instructional video captions"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "multimodal abstractive summarization for how2 videos"
    - "videobert a joint model for video and language representation learning"

Connected through hyperedge HE-6 (induced by source paper "modelling latent skills for multitask language generation"):
  with members:
    - "plug and play language models a simple approach to controlled text generation"
    - "guiding extractive summarization with question answering rewards"
    - "continual unsupervised representation learning"
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"
    - "putting machine translation in context with the noisy channel model"
    - "neural extractive text summarization with syntactic compression"
    - "text summarization with pretrained encoders"

Connected through hyperedge HE-7 (induced by source paper "realm retrieval augmented language model pre training"):
  with members:
    - "how much knowledge can you pack into the parameters of a language model"
    - "generalization through memorization nearest neighbor language models"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "language models as knowledge bases"
    - "latent retrieval for weakly supervised open domain question answering"
    - "knowledge guided text retrieval and reading for open domain question answering"
    - "spanbert improving pre training by representing and predicting spans"
    - "learning to retrieve reasoning paths over wikipedia graph for question answering"

Connected through hyperedge HE-8 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings"):
  with members:
    - "a primer in bertology what we know about how bert works"
    - "efficient sentence embedding using discrete cosine transform"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fair is better than sensational man is to doctor as woman is to doctor"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "assessing composition in sentence vector representations"

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)