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: "word mover s embedding from word2vec to document embedding"
  Abstract: While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised sentences or documents embeddings. Recent work has demonstrated that a distance measure between documents called \emph{Word Mover's Distance} (WMD) that aligns semantically similar words, yields unprecedented KNN classification accuracy. However, WMD is expensive to compute, and it is hard to extend its use beyond a KNN classifier. In this paper, we propose the \emph{Word Mover's Embedding } (WME), a novel approach to building an unsupervised document (sentence) embedding from pre-trained word embeddings. In our experiments on 9 benchmark text classification datasets and 22 textual similarity tasks, the proposed technique consistently matches or outperforms state-of-the-art techniques, with significantly higher accuracy on problems of short length.

Connected through hyperedge HE-1 (induced by source paper "constructing a highlight classifier with an attention based lstm neural network"):
  with members:
    - "personalized neural embeddings for collaborative filtering with text"
    - "deep bidirectional and unidirectional lstm recurrent neural network for network wide traffic speed prediction"
    - "deep learning for sentiment analysis a survey"
    - "generalizing word embeddings using bag of subwords"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "automatic music highlight extraction using convolutional recurrent attention networks"
    - "attention is all you need"
    - "comparative study of cnn and rnn for natural language processing"

Connected through hyperedge HE-2 (induced by source paper "structural aware sentence similarity with recursive optimal transport"):
  with members:
    - "don t settle for average go for the max fuzzy sets and max pooled word vectors"
    - "learning semantic textual similarity from conversations"
    - "context mover s distance barycenters optimal transport of contexts for building representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "parameter free sentence embedding via orthogonal basis"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning based text style transfer without parallel training corpus"):
  with members:
    - "improving reinforcement learning based image captioning with natural language prior"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "a study of reinforcement learning for neural machine translation"
    - "style transfer through back translation"
    - "shaped shared private encoder decoder for text style adaptation"
    - "polite dialogue generation without parallel data"
    - "graph2seq graph to sequence learning with attention based neural networks"

Connected through hyperedge HE-4 (induced by source paper "hierarchical document encoder for parallel corpus mining"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "margin based parallel corpus mining with multilingual sentence embeddings"
    - "learning cross lingual sentence representations via a multi task dual encoder model"
    - "efficient vector representation for documents through corruption"
    - "attention is all you need"
    - "exploiting cross sentence context for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "efficient global string kernel with random features beyond counting substructures"):
  with members:
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "scalable spectral clustering using random binning features"
    - "random warping series a random features method for time series embedding"
    - "lstm a search space odyssey"
    - "efficient approximation algorithms for string kernel based sequence classification"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "wasserstein fisher rao document distance"):
  with members:
    - "document similarity for texts of varying lengths via hidden topics"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "advances in pre training distributed word representations"
    - "using centroids of word embeddings and word mover s distance for biomedical document retrieval in question answering"
    - "skip thought vectors"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "speeding up word mover s distance and its variants via properties of distances between embeddings"):
  with members:
    - "linear complexity relaxed word mover s distance with gpu acceleration"
    - "document embedding with paragraph vectors"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "hierarchical optimal transport for document representation"):
  with members:
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "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)