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: "clustering comparable corpora of russian and ukrainian academic texts word embeddings and semantic fingerprints"
  Abstract: We present our experience in applying distributional semantics (neural word embeddings) to the problem of representing and clustering documents in a bilingual comparable corpus. Our data is a collection of Russian and Ukrainian academic texts, for which topics are their academic fields. In order to build language-independent semantic representations of these documents, we train neural distributional models on monolingual corpora and learn the optimal linear transformation of vectors from one language to another. The resulting vectors are then used to produce ‘semantic fingerprints’ of documents, serving as input to a clustering algorithm. The presented method is compared to several baselines including ‘orthographic translation’ with Levenshtein edit distance and outperforms them by a large margin. We also show that language-independent ‘semantic fingerprints’ are superior to multi-lingual clustering algorithms proposed in the previous work, at the same time requiring less linguistic resources.

Connected through hyperedge HE-1 (induced by source paper "russian word sense induction by clustering averaged word embeddings"):
  with members:
    - "word sense induction using word embeddings and community detection in complex networks"
    - "size vs structure in training corpora for word embedding models araneum russicum maximum and russian national corpus"
    - "unsupervised knowledge free and interpretable word sense disambiguation"
    - "enriching word vectors with subword information"
    - "breaking sticks and ambiguities with adaptive skip gram"

Connected through hyperedge HE-2 (induced by source paper "temporal dynamics of semantic relations in word embeddings an application to predicting armed conflict participants"):
  with members:
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "incremental skip gram model with negative sampling"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"

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)