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: "learning to compute word embeddings on the fly"
  Abstract: Words in natural language follow a Zipfian distribution whereby some words are frequent but most are rare. Learning representations for words in the ``long tail'' of this distribution requires enormous amounts of data. Representations of rare words trained directly on end tasks are usually poor, requiring us to pre-train embeddings on external data, or treat all rare words as out-of-vocabulary words with a unique representation. We provide a method for predicting embeddings of rare words on the fly from small amounts of auxiliary data with a network trained end-to-end for the downstream task. We show that this improves results against baselines where embeddings are trained on the end task for reading comprehension, recognizing textual entailment and language modeling.

Connected through hyperedge HE-1 (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-2 (induced by source paper "making neural machine reading comprehension faster"):
  with members:
    - "neural machine translation for bilingually scarce scenarios a deep multi task learning approach"
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "a fully attention based information retriever"
    - "lightweight convolutional approaches to reading comprehension on squad"
    - "phase conductor on multi layered attentions for machine comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "contextualized word representations for reading comprehension"
    - "smarnet teaching machines to read and comprehend like human"

Connected through hyperedge HE-3 (induced by source paper "latent relation language models"):
  with members:
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "transformer xl attentive language models beyond a fixed length context"
    - "bootstrapping generators from noisy data"
    - "neural lattice language models"
    - "describing a knowledge base"
    - "deep contextualized word representations"
    - "building language models for text with named entities"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"

Connected through hyperedge HE-4 (induced by source paper "adapting sequence to sequence models for text normalization in social media"):
  with members:
    - "improving text normalization by optimizing nearest neighbor matching"
    - "a nested attention neural hybrid model for grammatical error correction"
    - "get to the point summarization with pointer generator networks"
    - "bpemb tokenization free pre trained subword embeddings in 275 languages"
    - "monoise modeling noise using a modular normalization system"
    - "synthetic and natural noise both break neural machine translation"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "achieving open vocabulary neural machine translation with hybrid word character models"

Connected through hyperedge HE-5 (induced by source paper "a hybrid semantic parsing approach for tabular data analysis"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "the natural language decathlon multitask learning as question answering"
    - "semantic parsing with syntax and table aware sql generation"
    - "typesql knowledge based type aware neural text to sql generation"
    - "universal semantic parsing"
    - "mimicking word embeddings using subword rnns"
    - "a minimal span based neural constituency parser"

Connected through hyperedge HE-6 (induced by source paper "multi channel reverse dictionary model"):
  with members:
    - "enhancing recurrent neural networks with sememes"
    - "modeling semantic compositionality with sememe knowledge"
    - "open the boxes of words incorporating sememes into textual adversarial attack"
    - "openhownet an open sememe based lexical knowledge base"
    - "using multi sense vector embeddings for reverse dictionaries"
    - "incorporating glosses into neural word sense disambiguation"
    - "building a knowledge graph from natural language definitions for interpretable text entailment recognition"
    - "mapping text to knowledge graph entities using multi sense lstms"

Connected through hyperedge HE-7 (induced by source paper "embedding imputation with grounded language information"):
  with members:
    - "generalizing word embeddings using bag of subwords"
    - "card 660 cambridge rare word dataset a reliable benchmark for infrequent word representation models"
    - "how powerful are graph neural networks"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "mimicking word embeddings using subword rnns"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "estimator vectors oov word embeddings based on subword and context clue estimates"):
  with members:
    - "learning semantic representations for novel words leveraging both form and context"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mimicking word embeddings using subword rnns"
    - "all but the top simple and effective postprocessing for word representations"
    - "high risk learning acquiring new word vectors from tiny data"
    - "enriching word vectors with subword information"
    - "efficient estimation of word representations in vector space"

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.LG(Machine Learning)