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: "riemannian optimization for skip gram negative sampling"
  Abstract: Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in "word2vec" software, is usually optimized by stochastic gradient descent. However, the optimization of SGNS objective can be viewed as a problem of searching for a good matrix with the low-rank constraint. The most standard way to solve this type of problems is to apply Riemannian optimization framework to optimize the SGNS objective over the manifold of required low-rank matrices. In this paper, we propose an algorithm that optimizes SGNS objective using Riemannian optimization and demonstrates its superiority over popular competitors, such as the original method to train SGNS and SVD over SPPMI matrix.

Connected through hyperedge HE-1 (induced by source paper "revisiting skip gram negative sampling model with regularization"):
  with members:
    - "on the convergence of adam and beyond"
    - "understanding composition of word embeddings via tensor decomposition"
    - "advances in pre training distributed word representations"
    - "word2vec skip gram with negative sampling is a weighted logistic pca"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "greedy approaches to symmetric orthogonal tensor decomposition"
    - "neural architectures for named entity recognition"
    - "successive rank one approximations for nearly orthogonally decomposable symmetric tensors"

Connected through hyperedge HE-2 (induced by source paper "rotations and interpretability of word embeddings the case of the russian language"):
  with members:
    - "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"
    - "russe the first workshop on russian semantic similarity"
    - "efficient estimation of word representations in vector space"

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)