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: "multilingual distributed representations without word alignment"
  Abstract: Distributed representations of meaning are a natural way to encode covariance relationships between words and phrases in NLP. By overcoming data sparsity problems, as well as providing information about semantic relatedness which is not available in discrete representations, distributed representations have proven useful in many NLP tasks. Recent work has shown how compositional semantic representations can successfully be applied to a number of monolingual applications such as sentiment analysis. At the same time, there has been some initial success in work on learning shared word-level representations across languages. We combine these two approaches by proposing a method for learning distributed representations in a multilingual setup. Our model learns to assign similar embeddings to aligned sentences and dissimilar ones to sentence which are not aligned while not requiring word alignments. We show that our representations are semantically informative and apply them to a cross-lingual document classification task where we outperform the previous state of the art. Further, by employing parallel corpora of multiple language pairs we find that our model learns representations that capture semantic relationships across languages for which no parallel data was used.

Connected through hyperedge HE-1 (induced by source paper "learning robust representations via multi view information bottleneck"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "on mutual information maximization for representation learning"
    - "contrastive multiview coding"
    - "on variational bounds of mutual information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "deep variational canonical correlation analysis"

Connected through hyperedge HE-2 (induced by source paper "robust cross lingual embeddings from parallel sentences"):
  with members:
    - "crosslingual document embedding as reduced rank ridge regression"
    - "a survey of cross lingual word embedding models"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "better word embeddings by disentangling contextual n gram information"
    - "a corpus for multilingual document classification in eight languages"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "unsupervised multilingual word embeddings"

Connected through hyperedge HE-3 (induced by source paper "image search using multilingual texts a cross modal learning approach between image and text"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "simple recurrent units for highly parallelizable recurrence"
    - "vse improving visual semantic embeddings with hard negatives"
    - "word translation without parallel data"
    - "end to end learning of deep visual representations for image retrieval"
    - "aggregated residual transformations for deep neural networks"
    - "fully convolutional siamese networks for object tracking"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-4 (induced by source paper "on the contributions of visual and textual supervision in low resource semantic speech retrieval"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "towards visually grounded sub word speech unit discovery"
    - "symbolic inductive bias for visually grounded learning of spoken language"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "visually grounded cross lingual keyword spotting in speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"
    - "visually grounded learning of keyword prediction from untranscribed speech"

Connected through hyperedge HE-5 (induced by source paper "closed form word embedding alignment"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "consistent alignment of world embedding models"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "deepwalk online learning of social representations"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "best practices for learning domain specific cross lingual embeddings"):
  with members:
    - "unsupervised machine translation using monolingual corpora only"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "massively multilingual word embeddings"
    - "bidirectional lstm crf models for sequence tagging"
    - "bilingual distributed word representations from document aligned comparable data"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-7 (induced by source paper "char rnn for word stress detection in east slavic languages"):
  with members:
    - "automated word stress detection in russian"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "cross lingual character level neural morphological tagging"
    - "syntaxnet models for the conll 2017 shared task"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "massively multilingual word embeddings"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-8 (induced by source paper "learning embodied semantics via music and dance semiotic correlations"):
  with members:
    - "let s dance learning from online dance videos"
    - "everybody dance now"
    - "deep cross modal correlation learning for audio and lyrics in music retrieval"
    - "convolutional pose machines"
    - "multi view recurrent neural acoustic word embeddings"
    - "you only look once unified real time object detection"

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)