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: "think globally embed locally locally linear meta embedding of words"
  Abstract: Distributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks. However, their performances vary significantly across different tasks, implying that the word embeddings learnt by those methods capture complementary aspects of lexical semantics. Therefore, we believe that it is important to combine the existing word embeddings to produce more accurate and complete \emph{meta-embeddings} of words. For this purpose, we propose an unsupervised locally linear meta-embedding learning method that takes pre-trained word embeddings as the input, and produces more accurate meta embeddings. Unlike previously proposed meta-embedding learning methods that learn a global projection over all words in a vocabulary, our proposed method is sensitive to the differences in local neighbourhoods of the individual source word embeddings. Moreover, we show that vector concatenation, a previously proposed highly competitive baseline approach for integrating word embeddings, can be derived as a special case of the proposed method. Experimental results on semantic similarity, word analogy, relation classification, and short-text classification tasks show that our meta-embeddings to significantly outperform prior methods in several benchmark datasets, establishing a new state of the art for meta-embeddings.

Connected through hyperedge HE-1 (induced by source paper "meta embeddings based on self attention"):
  with members:
    - "time aware large kernel convolutions"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "improving neural language modeling via adversarial training"
    - "generating long sequences with sparse transformers"
    - "adaptive attention span in transformers"
    - "weighted transformer network for machine translation"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"

Connected through hyperedge HE-2 (induced by source paper "a common semantic space for monolingual and cross lingual meta embeddings"):
  with members:
    - "sequence tagging with contextual and non contextual subword representations a multilingual evaluation"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "deep contextualized word representations"
    - "bilingual embeddings with random walks over multilingual wordnets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a survey of word embeddings evaluation methods"
    - "advances in pre training distributed word representations"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"

Connected through hyperedge HE-3 (induced by source paper "hierarchical meta embeddings for code switching named entity recognition"):
  with members:
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "incorporating word and subword units in unsupervised machine translation using language model rescoring"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "code switching language modeling using syntax aware multi task learning"
    - "dynamic meta embeddings for improved sentence representations"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "learning word vectors for 157 languages"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "med2meta learning representations of medical concepts with meta embeddings"):
  with members:
    - "mixed pooling multi view attention autoencoder for representation learning in healthcare"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "drug similarity integration through attentive multi view graph auto encoders"
    - "medical concept representation learning from electronic health records and its application on heart failure prediction"
    - "gram graph based attention model for healthcare representation learning"
    - "learning word meta embeddings by using ensembles of embedding sets"
    - "a primer on neural network models for natural language processing"

Connected through hyperedge HE-5 (induced by source paper "semi supervised multi task word embeddings"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "a joint model for word embedding and word morphology"
    - "enriching word vectors with subword information"
    - "recurrent neural network for text classification with multi task learning"

Connected through hyperedge HE-6 (induced by source paper "meta embedding as auxiliary task regularization"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "a joint model for word embedding and word morphology"
    - "enriching word vectors with subword information"
    - "recurrent neural network for text classification with multi task learning"
    - "learning word meta embeddings by using ensembles of embedding sets"

Connected through hyperedge HE-7 (induced by source paper "double embeddings and cnn based sequence labeling for aspect extraction"):
  with members:
    - "lifelong learning crf for supervised aspect extraction"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "convolutional sequence to sequence learning"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "unsupervised word and dependency path embeddings for aspect term extraction"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "enriching word vectors with subword information"
    - "unsupervised cross domain word representation learning"

Connected through hyperedge HE-8 (induced by source paper "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"):
  with members:
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "the expressive power of word embeddings"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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)