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: "multilingual language processing from bytes"
  Abstract: We describe an LSTM-based model which we call Byte-to-Span (BTS) that reads text as bytes and outputs span annotations of the form [start, length, label] where start positions, lengths, and labels are separate entries in our vocabulary. Because we operate directly on unicode bytes rather than language-specific words or characters, we can analyze text in many languages with a single model. Due to the small vocabulary size, these multilingual models are very compact, but produce results similar to or better than the state-of- the-art in Part-of-Speech tagging and Named Entity Recognition that use only the provided training datasets (no external data sources). Our models are learning "from scratch" in that they do not rely on any elements of the standard pipeline in Natural Language Processing (including tokenization), and thus can run in standalone fashion on raw text.

Connected through hyperedge HE-1 (induced by source paper "using interlinear glosses as pivot in low resource multilingual machine translation"):
  with members:
    - "morphological word embeddings"
    - "character aware decoder for translation into morphologically rich languages"
    - "massively parallel cross lingual learning in low resource target language translation"
    - "improving character based decoding using target side morphological information for neural machine translation"
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"

Connected through hyperedge HE-2 (induced by source paper "improving pre trained multilingual models with vocabulary expansion"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "gromov wasserstein alignment of word embedding spaces"
    - "generating wikipedia by summarizing long sequences"
    - "bilingual expert can find translation errors"
    - "universal neural machine translation for extremely low resource languages"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "on the limitations of unsupervised bilingual dictionary induction"

Connected through hyperedge HE-3 (induced by source paper "comic toward a compact image captioning model with attention"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "contrastive learning for image captioning"
    - "massive exploration of neural machine translation architectures"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-4 (induced by source paper "hierarchical contextualized representation for named entity recognition"):
  with members:
    - "semantics aware bert for language understanding"
    - "grn gated relation network to enhance convolutional neural network for named entity recognition"
    - "syntax aware multilingual semantic role labeling"
    - "lattice based transformer encoder for neural machine translation"
    - "gcdt a global context enhanced deep transition architecture for sequence labeling"
    - "dual co matching network for multi choice reading comprehension"
    - "robust lexical features for improved neural network named entity recognition"
    - "contextualized non local neural networks for sequence learning"

Connected through hyperedge HE-5 (induced by source paper "tabula nearly rasa probing the linguistic knowledge of character level neural language models trained on unsegmented text"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "explaining character aware neural networks for word level prediction do they discover linguistic rules"
    - "assessing composition in sentence vector representations"
    - "an analysis of neural language modeling at multiple scales"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "recurrent neural networks in linguistic theory revisiting pinker and prince 1988 and the past tense debate"
    - "learning to generate reviews and discovering sentiment"

Connected through hyperedge HE-6 (induced by source paper "neural machine translation with byte level subwords"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "language models with transformers"
    - "revisiting character based neural machine translation with capacity and compression"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "when and why are pre trained word embeddings useful for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "a morpho syntactically informed lstm crf model for named entity recognition"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "application of a hybrid bi lstm crf model to the task of russian named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "a survey on recent advances in named entity recognition from deep learning models"):
  with members:
    - "neuroner an easy to use program for named entity recognition based on neural networks"
    - "end to end recurrent neural network models for vietnamese named entity recognition word level vs character level"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "robust multilingual named entity recognition with shallow semi supervised features"
    - "an investigation of recurrent neural architectures for drug name recognition"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "multi task cross lingual sequence tagging from scratch"

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)