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: "bilingual lexicon induction with semi supervision in non isometric embedding spaces"
  Abstract: Recent work on bilingual lexicon induction (BLI) has frequently depended either on aligned bilingual lexicons or on distribution matching, often with an assumption about the isometry of the two spaces. We propose a technique to quantitatively estimate this assumption of the isometry between two embedding spaces and empirically show that this assumption weakens as the languages in question become increasingly etymologically distant. We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) --- a semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique. Our proposed method obtains state of the art results on 15 of 18 language pairs on the MUSE dataset, and does particularly well when the embedding spaces don't appear to be isometric. In addition, we also show that adding supervision stabilizes the learning procedure, and is effective even with minimal supervision.

Connected through hyperedge HE-1 (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"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "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-2 (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"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"

Connected through hyperedge HE-3 (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"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "investigating multilingual nmt representations at scale"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"

Connected through hyperedge HE-4 (induced by source paper "should all cross lingual embeddings speak english"):
  with members:
    - "don t forget the long tail a comprehensive analysis of morphological generalization in bilingual lexicon induction"
    - "density matching for bilingual word embedding"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"
    - "lost in evaluation misleading benchmarks for bilingual dictionary induction"
    - "unsupervised statistical machine translation"
    - "unsupervised multilingual word embeddings"
    - "learning word vectors for 157 languages"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

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)