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: "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
  Abstract: This paper describes SentencePiece, a language-independent subword tokenizer and detokenizer designed for Neural-based text processing, including Neural Machine Translation. It provides open-source C++ and Python implementations for subword units. While existing subword segmentation tools assume that the input is pre-tokenized into word sequences, SentencePiece can train subword models directly from raw sentences, which allows us to make a purely end-to-end and language independent system. We perform a validation experiment of NMT on English-Japanese machine translation, and find that it is possible to achieve comparable accuracy to direct subword training from raw sentences. We also compare the performance of subword training and segmentation with various configurations. SentencePiece is available under the Apache 2 license at this https URL.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "fastai a layered api for deep learning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "developing an app to interpret chest x rays to support the diagnosis of respiratory pathology with artificial intelligence"
    - "suggestion mining from online reviews using ulmfit"
    - "multiclass language identification using deep learning on spectral images of audio signals"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "mish a self regularized non monotonic neural activation function"
    - "bag of tricks for image classification with convolutional neural networks"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"

Connected through hyperedge HE-4 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition"):
  with members:
    - "q8bert quantized 8bit bert"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "a comparative study on transformer vs rnn in speech applications"
    - "language modeling with deep transformers"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "fully quantized transformer for improved translation"
    - "streaming end to end speech recognition for mobile devices"

Connected through hyperedge HE-5 (induced by source paper "what the mask making sense of language specific bert models"):
  with members:
    - "arabert transformer based model for arabic language understanding"
    - "binary and multitask classification model for dutch anaphora resolution die dat prediction"
    - "robbert a dutch roberta based language model"
    - "a primer in bertology what we know about how bert works"
    - "camembert a tasty french language model"
    - "albert a lite bert for self supervised learning of language representations"
    - "paws x a cross lingual adversarial dataset for paraphrase identification"
    - "flaubert unsupervised language model pre training for french"

Connected through hyperedge HE-6 (induced by source paper "boosting factual correctness of abstractive summarization with knowledge graph"):
  with members:
    - "passage re ranking with bert"
    - "evaluating the factual consistency of abstractive text summarization"
    - "make lead bias in your favor a simple and effective method for news summarization"
    - "assessing the factual accuracy of generated text"
    - "text summarization with pretrained encoders"
    - "unified language model pre training for natural language understanding and generation"
    - "optimizing the factual correctness of a summary a study of summarizing radiology reports"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-7 (induced by source paper "normalization of input output shared embeddings in text generation models"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "levenshtein transformer"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "transformers without tears improving the normalization of self attention"

Connected through hyperedge HE-8 (induced by source paper "graph constrained reinforcement learning for natural language action spaces"):
  with members:
    - "nail a general interactive fiction agent"
    - "interactive fiction games a colossal adventure"
    - "toward automated quest generation in text adventure games"
    - "interactive language learning by question answering"
    - "learning to speak and act in a fantasy text adventure game"
    - "comprehensible context driven text game playing"
    - "transfer in deep reinforcement learning using knowledge graphs"
    - "using reinforcement learning to learn how to play text based games"

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)