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: "a survey of word embeddings evaluation methods"
  Abstract: Word embeddings are real-valued word representations able to capture lexical semantics and trained on natural language corpora. Models proposing these representations have gained popularity in the recent years, but the issue of the most adequate evaluation method still remains open. This paper presents an extensive overview of the field of word embeddings evaluation, highlighting main problems and proposing a typology of approaches to evaluation, summarizing 16 intrinsic methods and 12 extrinsic methods. I describe both widely-used and experimental methods, systematize information about evaluation datasets and discuss some key challenges.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "an analysis of word2vec for the italian language"):
  with members:
    - "neural information retrieval a literature review"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "deep learning for sentiment analysis a survey"
    - "code2vec learning distributed representations of code"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "morphosyntactic tagging with a meta bilstm model over context sensitive token encodings"
    - "generalizing word embeddings using bag of subwords"
    - "dynamic meta embeddings for improved sentence representations"

Connected through hyperedge HE-4 (induced by source paper "attributed graph learning with 2 d graph convolution"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "gmnn graph markov neural networks"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "attributed graph clustering via adaptive graph convolution"
    - "learning discrete structures for graph neural networks"
    - "label efficient semi supervised learning via graph filtering"
    - "a flexible generative framework for graph based semi supervised learning"
    - "graph neural networks a review of methods and applications"

Connected through hyperedge HE-5 (induced by source paper "evaluating word embedding models methods and experimental results"):
  with members:
    - "cross lingual language model pretraining"
    - "unsupervised statistical machine translation"
    - "semantic structure and interpretability of word embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "post processing of word representations via variance normalization and dynamic embedding"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "shareable representations for search query understanding"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "transfer learning for sequences via learning to collocate"
    - "linguistic knowledge and transferability of contextual representations"
    - "unsupervised multilingual word embeddings"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"

Connected through hyperedge HE-7 (induced by source paper "sentence embeddings for russian nlu"):
  with members:
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "catboost unbiased boosting with categorical features"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-8 (induced by source paper "word embeddings for entity annotated texts"):
  with members:
    - "verse versatile graph embeddings from similarity measures"
    - "efficient graph computation for node2vec"
    - "graph embedding techniques applications and performance a survey"
    - "query expansion with locally trained word embeddings"
    - "node2vec scalable feature learning for networks"
    - "enriching word vectors with subword information"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "line large scale information network embedding"

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)