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: "texts in meaning out neural language models in semantic similarity task for russian"
  Abstract: Distributed vector representations for natural language vocabulary get a lot of attention in contemporary computational linguistics. This paper summarizes the experience of applying neural network language models to the task of calculating semantic similarity for Russian. The experiments were performed in the course of Russian Semantic Similarity Evaluation track, where our models took from the 2nd to the 5th position, depending on the task. #R##N#We introduce the tools and corpora used, comment on the nature of the shared task and describe the achieved results. It was found out that Continuous Skip-gram and Continuous Bag-of-words models, previously successfully applied to English material, can be used for semantic modeling of Russian as well. Moreover, we show that texts in Russian National Corpus (RNC) provide an excellent training material for such models, outperforming other, much larger corpora. It is especially true for semantic relatedness tasks (although stacking models trained on larger corpora on top of RNC models improves performance even more). #R##N#High-quality semantic vectors learned in such a way can be used in a variety of linguistic tasks and promise an exciting field for further study.

Connected through hyperedge HE-1 (induced by source paper "distributional semantic modeling a revised technique to train term word vector space models applying the ontology related approach"):
  with members:
    - "development of formal models algorithms procedures engineering and functioning of the software system instrumental complex for ontological engineering purpose"
    - "deep contextualized word representations"
    - "integrated tools for engineering ontologies"
    - "to the problem of the instrumental complex for ontological engineering purpose software system design"
    - "research and development workstation environment the new class of current research information systems"
    - "word2vec applied to recommendation hyperparameters matter"
    - "technique for designing a domain ontology"
    - "fasttext zip compressing text classification models"

Connected through hyperedge HE-2 (induced by source paper "wsd algorithm based on a new method of vector word contexts proximity calculation via epsilon filtration"):
  with members:
    - "calculated attributes of synonym sets"
    - "linking imagenet wordnet synsets with wikidata"
    - "analysis of wikipedia based corpora for question answering"

Connected through hyperedge HE-3 (induced by source paper "size vs structure in training corpora for word embedding models araneum russicum maximum and russian national corpus"):
  with members:
    - "improving reliability of word similarity evaluation by redesigning annotation task and performance measure"
    - "a primer on neural network models for natural language processing"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "distributed representations of words and phrases and their compositionality"
    - "a universal part of speech tagset"
    - "from frequency to meaning vector space models of semantics"

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)