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: "fast low rank matrix learning with nonconvex regularization"
  Abstract: Low-rank modeling has a lot of important applications in machine learning, computer vision and social network analysis. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better recovery performance. However, the resultant optimization problem is much more challenging. A very recent state-of-the-art is based on the proximal gradient algorithm. However, it requires an expensive full SVD in each proximal step. In this paper, we show that for many commonly-used nonconvex low-rank regularizers, a cutoff can be derived to automatically threshold the singular values obtained from the proximal operator. This allows the use of power method to approximate the SVD efficiently. Besides, the proximal operator can be reduced to that of a much smaller matrix projected onto this leading subspace. Convergence, with a rate of O(1/T) where T is the number of iterations, can be guaranteed. Extensive experiments are performed on matrix completion and robust principal component analysis. The proposed method achieves significant speedup over the state-of-the-art. Moreover, the matrix solution obtained is more accurate and has a lower rank than that of the traditional nuclear norm regularizer.

Connected through hyperedge HE-1 (induced by source paper "adaptive structure constrained robust latent low rank coding for image recovery"):
  with members:
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "subspace clustering by block diagonal representation"
    - "object classification with joint projection and low rank dictionary learning"
    - "making a science of model search"
    - "robust principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "learning with heterogeneous side information fusion for recommender systems"):
  with members:
    - "taking human out of learning applications a survey on automated machine learning"
    - "heteromed heterogeneous information network for medical diagnosis"
    - "collaborative filtering with social local models"
    - "heterogeneous information network embedding for recommendation"
    - "wide deep learning for recommender systems"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "a survey of heterogeneous information network analysis"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "large scale low rank matrix learning with nonconvex regularizers"):
  with members:
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "orthogonal rank one matrix pursuit for low rank matrix completion"
    - "generalized singular value thresholding"
    - "robust recovery of subspace structures by low rank representation"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-4 (induced by source paper "learning of generalized low rank models a greedy approach"):
  with members:
    - "large scale multi label learning with missing labels"
    - "large scale convex minimization with a low rank constraint"
    - "generalized boosting algorithms for convex optimization"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "can dmd obtain a scene background in color"
    - "efficient background modeling based on sparse representation and outlier iterative removal"
    - "fast algorithms for robust pca via gradient descent"
    - "fast robust pca on graphs"
    - "approximated robust principal component analysis for improved general scene background subtraction"
    - "mpi faun an mpi based framework for alternating updating nonnegative matrix factorization"

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.NA(Numerical Analysis)