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 dual framework for low rank tensor completion"
  Abstract: We propose a novel formulation of the low-rank tensor completion problem that is based on the duality theory and a particular choice of low-rank regularizer. This low-rank regularizer along with the dual perspective provides a simple characterization of the solution to the tensor completion problem. Motivated by large-scale setting, we next derive a rank-constrained reformulation of the proposed optimization problem, which is shown to lie on the Riemannian spectrahedron manifold. We exploit the versatile Riemannian optimization framework to develop computationally efficient conjugate gradient and trust-region algorithms. The experiments confirm the benefits of our choice of regularization and the proposed algorithms outperform state-of-the-art algorithms on several real-world data sets in different applications.

Connected through hyperedge HE-1 (induced by source paper "scaling up collaborative filtering data sets through randomized fractal expansions"):
  with members:
    - "scalable realistic recommendation datasets through fractal expansions"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "neural collaborative filtering"
    - "structural deep embedding for hyper networks"
    - "efficient softmax approximation for gpus"
    - "a neural autoregressive approach to collaborative filtering"

Connected through hyperedge HE-2 (induced by source paper "scalable realistic recommendation datasets through fractal expansions"):
  with members:
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "neural collaborative filtering"
    - "structural deep embedding for hyper networks"
    - "a neural autoregressive approach to collaborative filtering"
    - "scikit image image processing in python"
    - "collaborative deep learning for recommender systems"
    - "challenging the long tail recommendation"

Connected through hyperedge HE-3 (induced by source paper "riemannian adaptive stochastic gradient algorithms on matrix manifolds"):
  with members:
    - "on the convergence of adam and beyond"
    - "shampoo preconditioned stochastic tensor optimization"
    - "geomstats a python package for riemannian geometry in machine learning"
    - "riemannian approach to batch normalization"
    - "poincar e embeddings for learning hierarchical representations"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"
    - "adaptive bound optimization for online convex optimization"

Connected through hyperedge HE-4 (induced by source paper "faster fast tensor completion with nonconvex regularization"):
  with members:
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "large scale low rank matrix learning with nonconvex regularizers"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "low rank tensor recovery via iterative hard thresholding"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "parallel matrix factorization for low rank tensor completion"
    - "exact tensor completion using t svd"

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.LG(Machine Learning)