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: "russe the first workshop on russian semantic similarity"
  Abstract: The paper gives an overview of the Russian Semantic Similarity Evaluation (RUSSE) shared task held in conjunction with the Dialogue 2015 conference. There exist a lot of comparative studies on semantic similarity, yet no analysis of such measures was ever performed for the Russian language. Exploring this problem for the Russian language is even more interesting, because this language has features, such as rich morphology and free word order, which make it signi cantly di erent from English, German, and other well-studied languages. We attempt to bridge this gap by proposing a shared task on the semantic similarity of Russian nouns. Our key contribution is an evaluation methodology based on four novel benchmark datasets for the Russian language. Our analysis of the 105 submissions from 19 teams reveals that successful approaches for English, such as distributional and skip-gram models, are directly applicable to Russian as well. On the one hand, the best results in the contest were obtained by sophisticated supervised models that combine evidence from di erent sources. On the other hand, completely unsupervised approaches, such as a skip-gram model estimated on a large-scale corpus, were able score among the top 5 systems.

Connected through hyperedge HE-1 (induced by source paper "construction and quality evaluation of heterogeneous hierarchical topic models"):
  with members:
    - "scalable and robust construction of topical hierarchies"
    - "probabilistic latent semantic analysis"
    - "visualizing streaming text data with dynamic maps"

Connected through hyperedge HE-2 (induced by source paper "rotations and interpretability of word embeddings the case of the russian language"):
  with members:
    - "riemannian optimization for skip gram negative sampling"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "all but the top simple and effective postprocessing for word representations"
    - "human and machine judgements for russian semantic relatedness"
    - "enriching word vectors with subword information"
    - "correlation based intrinsic evaluation of word vector representations"
    - "compressing word embeddings"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "human and machine judgements for russian semantic relatedness"):
  with members:
    - "morphological analyzer and generator for russian and ukrainian languages"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "distributed representations of words and phrases and their compositionality"
    - "text relatedness based on a word thesaurus"

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)