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: "analyzing the limitations of cross lingual word embedding mappings"
  Abstract: Recent research in cross-lingual word embeddings has almost exclusively focused on offline methods, which independently train word embeddings in different languages and map them to a shared space through linear transformations. While several authors have questioned the underlying isomorphism assumption, which states that word embeddings in different languages have approximately the same structure, it is not clear whether this is an inherent limitation of mapping approaches or a more general issue when learning cross-lingual embeddings. So as to answer this question, we experiment with parallel corpora, which allows us to compare offline mapping to an extension of skip-gram that jointly learns both embedding spaces. We observe that, under these ideal conditions, joint learning yields to more isomorphic embeddings, is less sensitive to hubness, and obtains stronger results in bilingual lexicon induction. We thus conclude that current mapping methods do have strong limitations, calling for further research to jointly learn cross-lingual embeddings with a weaker cross-lingual signal.

Connected through hyperedge HE-1 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs"):
  with members:
    - "a survey on knowledge graphs representation acquisition and applications"
    - "knowledge graph embedding for link prediction a comparative analysis"
    - "multi view knowledge graph embedding for entity alignment"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "jointly learning entity and relation representations for entity alignment"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "aligning cross lingual entities with multi aspect information"

Connected through hyperedge HE-2 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence"):
  with members:
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "a survey of cross lingual word embedding models"
    - "what do language representations really represent"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"
    - "investigating multilingual nmt representations at scale"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "do we really need fully unsupervised cross lingual embeddings"
    - "gromov wasserstein alignment of word embedding spaces"

Connected through hyperedge HE-3 (induced by source paper "cross lingual alignment vs joint training a comparative study and a simple unified framework"):
  with members:
    - "adversarial learning with contextual embeddings for zero resource cross lingual classification and ner"
    - "a survey of cross lingual word embedding models"
    - "bilingual lexicon induction through unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "density matching for bilingual word embedding"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"

Connected through hyperedge HE-4 (induced by source paper "emerging cross lingual structure in pretrained language models"):
  with members:
    - "similarity of neural network representations revisited"
    - "cross lingual bert transformation for zero shot dependency parsing"
    - "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
    - "polyglot contextual representations improve crosslingual transfer"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"
    - "investigating multilingual nmt representations at scale"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"

Connected through hyperedge HE-5 (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"
    - "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"
    - "non adversarial unsupervised word translation"

Connected through hyperedge HE-6 (induced by source paper "do we really need fully unsupervised cross lingual embeddings"):
  with members:
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "a survey of cross lingual word embedding models"
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gromov wasserstein alignment of word embedding spaces"
    - "unsupervised multilingual word embeddings"
    - "generalizing procrustes analysis for better bilingual dictionary induction"
    - "a resource light method for cross lingual semantic textual similarity"

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)