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: "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
  Abstract: Creating accurate meta-embeddings from pre-trained source embeddings has received attention lately. Methods based on global and locally-linear transformation and concatenation have shown to produce accurate meta-embeddings. In this paper, we show that the arithmetic mean of two distinct word embedding sets yields a performant meta-embedding that is comparable or better than more complex meta-embedding learning methods. The result seems counter-intuitive given that vector spaces in different source embeddings are not comparable and cannot be simply averaged. We give insight into why averaging can still produce accurate meta-embedding despite the incomparability of the source vector spaces.

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"
    - "self attention with relative position representations"

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"
    - "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"
    - "think globally embed locally locally linear meta embedding of words"
    - "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 "combining pretrained high resource embeddings and subword representations for low resource languages"):
  with members:
    - "learning word vectors for 157 languages"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "opennmt open source toolkit for neural machine translation"
    - "enriching word vectors with subword information"
    - "learning to understand phrases by embedding the dictionary"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "topic sensitive attention on generic corpora corrects sense bias in pretrained embeddings"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "projecting embeddings for domain adaptation joint modeling of sentiment analysis in diverse domains"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "question answering through transfer learning from large fine grained supervision data"

Connected through hyperedge HE-5 (induced by source paper "fusing vector space models for domain specific applications"):
  with members:
    - "lifelong domain word embedding via meta learning"
    - "deep contextualized word representations"
    - "phrase based neural unsupervised machine translation"
    - "comparative study of cnn and rnn for natural language processing"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "multichannel variable size convolution for sentence classification"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-6 (induced by source paper "sentence meta embeddings for unsupervised semantic textual similarity"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "structbert incorporating language structures into pre training for deep language understanding"
    - "improving sentence representations with multi view frameworks"
    - "angular based word meta embedding learning"
    - "dynamic meta embeddings for improved sentence representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-7 (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"
    - "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"
    - "think globally embed locally locally linear meta embedding of words"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "supervised contextual embeddings for transfer learning in natural language processing tasks"):
  with members:
    - "polyglot semantic role labeling"
    - "linguistically informed self attention for semantic role labeling"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a minimal span based neural constituency parser"
    - "learned in translation contextualized word vectors"
    - "semi supervised sequence tagging with bidirectional language models"
    - "neural skill transfer from supervised language tasks to reading comprehension"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

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)