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: "a generative word embedding model and its low rank positive semidefinite solution"
  Abstract: Most existing word embedding methods can be categorized into Neural Embedding Models and Matrix Factorization (MF)-based methods. However some models are opaque to probabilistic interpretation, and MF-based methods, typically solved using Singular Value Decomposition (SVD), may incur loss of corpus information. In addition, it is desirable to incorporate global latent factors, such as topics, sentiments or writing styles, into the word embedding model. Since generative models provide a principled way to incorporate latent factors, we propose a generative word embedding model, which is easy to interpret, and can serve as a basis of more sophisticated latent factor models. The model inference reduces to a low rank weighted positive semidefinite approximation problem. Its optimization is approached by eigendecomposition on a submatrix, followed by online blockwise regression, which is scalable and avoids the information loss in SVD. In experiments on 7 common benchmark datasets, our vectors are competitive to word2vec, and better than other MF-based methods.

Connected through hyperedge HE-1 (induced by source paper "sketching transformed matrices with applications to natural language processing"):
  with members:
    - "solving tall dense linear programs in nearly linear time"
    - "tight bounds for the subspace sketch problem with applications"
    - "optimal sketching for kronecker product regression and low rank approximation"
    - "a near optimal algorithm for approximating the john ellipsoid"
    - "regularized weighted low rank approximation"
    - "robust and sample optimal algorithms for psd low rank approximation"
    - "dimensionality reduction for tukey regression"
    - "total least squares regression in input sparsity time"

Connected through hyperedge HE-2 (induced by source paper "boosting the rating prediction with click data and textual contents"):
  with members:
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "a probabilistic model for the cold start problem in rating prediction using click data"
    - "item2vec neural item embedding for collaborative filtering"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "distributed representations of sentences and documents"
    - "collaborative deep learning for recommender systems"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "npe neural personalized embedding for collaborative filtering"):
  with members:
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "adam a method for stochastic optimization"
    - "collaborative deep learning for recommender systems"
    - "distributed representations of words and phrases and their compositionality"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "psdvec a toolbox for incremental and scalable word embedding"):
  with members:
    - "sparse overcomplete word vector representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "generative topic embedding a continuous representation of documents extended version with proofs"):
  with members:
    - "improving topic models with latent feature word representations"
    - "psdvec a toolbox for incremental and scalable word embedding"
    - "distributed representations of sentences and documents"
    - "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)