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 randomized singular value thresholding for low rank optimization"
  Abstract: Rank minimization can be converted into tractable surrogate problems, such as Nuclear Norm Minimization (NNM) and Weighted NNM (WNNM). The problems related to NNM, or WNNM, can be solved iteratively by applying a closed-form proximal operator, called Singular Value Thresholding (SVT), or Weighted SVT, but they suffer from high computational cost of Singular Value Decomposition (SVD) at each iteration. We propose a fast and accurate approximation method for SVT, that we call fast randomized SVT (FRSVT), with which we avoid direct computation of SVD. The key idea is to extract an approximate basis for the range of the matrix from its compressed matrix. Given the basis, we compute partial singular values of the original matrix from the small factored matrix. In addition, by developping a range propagation method, our method further speeds up the extraction of approximate basis at each iteration. Our theoretical analysis shows the relationship between the approximation bound of SVD and its effect to NNM via SVT. Along with the analysis, our empirical results quantitatively and qualitatively show that our approximation rarely harms the convergence of the host algorithms. We assess the efficiency and accuracy of the proposed method on various computer vision problems, e.g., subspace clustering, weather artifact removal, and simultaneous multi-image alignment and rectification.

Connected through hyperedge HE-1 (induced by source paper "study of compressed randomized utv decompositions for low rank matrix approximations in data science"):
  with members:
    - "subspace orbit randomized decomposition for low rank matrix approximations"
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "widely linear precoding for large scale mimo with iqi algorithms and performance analysis"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "practical sketching algorithms for low rank matrix approximation"
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"
    - "robust adaptive beamforming based on low rank and cross correlation techniques"
    - "adaptive reduced rank receive processing based on minimum symbol error rate criterion for large scale multiple antenna systems"

Connected through hyperedge HE-2 (induced by source paper "learning hybrid representation by robust dictionary learning in factorized compressed space"):
  with members:
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "learning structured twin incoherent twin projective latent dictionary pairs for classification"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "discriminative local sparse representation by robust adaptive dictionary pair learning"
    - "joint subspace recovery and enhanced locality driven robust flexible discriminative dictionary learning"
    - "flexible auto weighted local coordinate concept factorization a robust framework for unsupervised clustering"
    - "pointflownet learning representations for rigid motion estimation from point clouds"

Connected through hyperedge HE-3 (induced by source paper "compressed randomized utv decompositions for low rank approximations and big data applications"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "widely linear precoding for large scale mimo with iqi algorithms and performance analysis"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "practical sketching algorithms for low rank matrix approximation"
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"
    - "robust adaptive beamforming based on low rank and cross correlation techniques"
    - "adaptive reduced rank receive processing based on minimum symbol error rate criterion for large scale multiple antenna systems"

Connected through hyperedge HE-4 (induced by source paper "subspace orbit randomized decomposition for low rank matrix approximations"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "practical sketching algorithms for low rank matrix approximation"
    - "robust subspace clustering"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "robust principal component analysis exact recovery of corrupted low rank matrices"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "personalized cinemagraphs using semantic understanding and collaborative learning"):
  with members:
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "turning an urban scene video into a cinemagraph"
    - "tgif a new dataset and benchmark on animated gif description"
    - "video2gif automatic generation of animated gifs from video"
    - "instance aware semantic segmentation via multi task network cascades"
    - "convolutional feature masking for joint object and stuff segmentation"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "learning spatiotemporal features with 3d convolutional networks"

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.CV(Computer Vision and Pattern Recognition)