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: "human and machine judgements for russian semantic relatedness"
  Abstract: Semantic relatedness of terms represents similarity of meaning by a numerical score. On the one hand, humans easily make judgements about semantic relatedness. On the other hand, this kind of information is useful in language processing systems. While semantic relatedness has been extensively studied for English using numerous language resources, such as associative norms, human judgements and datasets generated from lexical databases, no evaluation resources of this kind have been available for Russian to date. Our contribution addresses this problem. We present five language resources of different scale and purpose for Russian semantic relatedness, each being a list of triples \(({word}_{i}, {word}_{j}, {similarity}_{ij}\)). Four of them are designed for evaluation of systems for computing semantic relatedness, complementing each other in terms of the semantic relation type they represent. These benchmarks were used to organise a shared task on Russian semantic relatedness, which attracted 19 teams. We use one of the best approaches identified in this competition to generate the fifth high-coverage resource, the first open distributional thesaurus of Russian. Multiple evaluations of this thesaurus, including a large-scale crowdsourcing study involving native speakers, indicate its high accuracy.

Connected through hyperedge HE-1 (induced by source paper "watset local global graph clustering with applications in sense and frame induction"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "unsupervised semantic frame induction using triclustering"
    - "a framework for enriching lexical semantic resources with distributional semantics"
    - "fighting with the sparsity of synonymy dictionaries for automatic synset induction"
    - "improving hypernymy extraction with distributional semantic classes"
    - "watset automatic induction of synsets from a graph of synonyms"
    - "breaking sticks and ambiguities with adaptive skip gram"

Connected through hyperedge HE-2 (induced by source paper "interpretable probabilistic embeddings bridging the gap between topic models and neural networks"):
  with members:
    - "improving topic models with latent feature word representations"
    - "separated by an un common language towards judgment language informed vector space modeling"
    - "skip thought vectors"
    - "document embedding with paragraph vectors"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "distributed representations of sentences and documents"
    - "word network topic model a simple but general solution for short and imbalanced texts"
    - "probabilistic latent semantic analysis"

Connected through hyperedge HE-3 (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"
    - "enriching word vectors with subword information"
    - "correlation based intrinsic evaluation of word vector representations"
    - "compressing word embeddings"
    - "russe the first workshop on russian semantic similarity"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "fighting with the sparsity of synonymy dictionaries for automatic synset induction"):
  with members:
    - "watset automatic induction of synsets from a graph of synonyms"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "automated detection of non relevant posts on the russian imageboard 2ch importance of the choice of word representations"):
  with members:
    - "enriching word vectors with subword information"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "distributed representations of words and phrases and their compositionality"

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)