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 discriminative latent variable model for bilingual lexicon induction"
  Abstract: We introduce a novel discriminative latent-variable model for the task of bilingual lexicon induction. Our model combines the bipartite matching dictionary prior of Haghighi et al. (2008) with a state-of-the-art embedding-based approach. To train the model, we derive an efficient Viterbi EM algorithm. We provide empirical improvements on six language pairs under two metrics and show that the prior theoretically and empirically helps to mitigate the hubness problem. We also demonstrate how previous work may be viewed as a similarly fashioned latent-variable model, albeit with a different prior.

Connected through hyperedge HE-1 (induced by source paper "refinement of unsupervised cross lingual word embeddings"):
  with members:
    - "a resource free evaluation metric for cross lingual word embeddings based on graph modularity"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gromov wasserstein alignment of word embedding spaces"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "enriching word vectors with subword information"
    - "massively multilingual word embeddings"
    - "ridge regression hubness and zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "are girls neko or sh ojo cross lingual alignment of non isomorphic embeddings with iterative normalization"):
  with members:
    - "a resource free evaluation metric for cross lingual word embeddings based on graph modularity"
    - "a survey of cross lingual word embedding models"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gromov wasserstein alignment of word embedding spaces"
    - "learning multilingual word embeddings in latent metric space a geometric approach"
    - "non adversarial unsupervised word translation"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-3 (induced by source paper "aligning vector spaces with noisy supervised lexicons"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "projecting embeddings for domain adaptation joint modeling of sentiment analysis in diverse domains"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "unsupervised cross lingual transfer of word embedding spaces"
    - "unsupervised machine translation using monolingual corpora only"
    - "unsupervised neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "word translation without parallel data"

Connected through hyperedge HE-4 (induced by source paper "don t forget the long tail a comprehensive analysis of morphological generalization in bilingual lexicon induction"):
  with members:
    - "learning word vectors for 157 languages"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "enriching word vectors with subword information"
    - "improving zero shot learning by mitigating the hubness problem"

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)