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: "interactive refinement of cross lingual word embeddings"
  Abstract: Cross-lingual word embeddings transfer knowledge between languages: models trained for a high-resource language can be used in a low-resource language. These embeddings are usually trained on general-purpose corpora but used for a domain-specific task. We introduce CLIME, an interactive system that allows a user to quickly adapt cross-lingual word embeddings for a given classification problem. First, words in the vocabulary are ranked by their salience to the downstream task. Then, salient keywords are displayed on an interface. Users mark the similarity between each keyword and its nearest neighbors in the embedding space. Finally, CLIME updates the embeddings using the annotations. We experiment clime on a cross-lingual text classification benchmark for four low-resource languages: Ilocano, Sinhalese, Tigrinya, and Uyghur. Embeddings refined by CLIME capture more nuanced word semantics and have higher test accuracy than the original embeddings. CLIME also improves test accuracy faster than an active learning baseline, and a simple combination of CLIME with active learning has the highest test accuracy.

Connected through hyperedge HE-1 (induced by source paper "exploiting cross lingual subword similarities in low resource document classification"):
  with members:
    - "don t forget the long tail a comprehensive analysis of morphological generalization in bilingual lexicon induction"
    - "a resource free evaluation metric for cross lingual word embeddings based on graph modularity"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "gromov wasserstein alignment of word embedding spaces"
    - "zero shot neural transfer for cross lingual entity linking"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "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)