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: "connections between nuclear norm and frobenius norm based representations"
  Abstract: A lot of works have shown that frobenius-norm-based representation (FNR) is competitive to sparse representation and nuclear-norm-based representation (NNR) in numerous tasks such as subspace clustering. Despite the success of FNR in experimental studies, less theoretical analysis is provided to understand its working mechanism. In this brief, we fill this gap by building the theoretical connections between FNR and NNR. More specially, we prove that: 1) when the dictionary can provide enough representative capacity, FNR is exactly NNR even though the data set contains the Gaussian noise, Laplacian noise, or sample-specified corruption and 2) otherwise, FNR and NNR are two solutions on the column space of the dictionary.

Connected through hyperedge HE-1 (induced by source paper "deep dictionary learning a parametric network approach"):
  with members:
    - "structured analysis dictionary learning for image classification"
    - "fast low rank shared dictionary learning for image classification"
    - "opening the black box of deep neural networks via information"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "universal adversarial perturbations"
    - "discriminative bayesian dictionary learning for classification"
    - "deep residual learning for image recognition"
    - "deepfool a simple and accurate method to fool deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "robust graph learning from noisy data"):
  with members:
    - "low rank kernel learning for graph based clustering"
    - "similarity learning via kernel preserving embedding"
    - "yotube searching action proposal via recurrent and static regression networks"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "robust principal component analysis on graphs"
    - "unsupervised feature selection with adaptive structure learning"
    - "robust pca via nonconvex rank approximation"

Connected through hyperedge HE-3 (induced by source paper "deep latent factor model for collaborative filtering"):
  with members:
    - "neural collaborative filtering"
    - "a deep matrix factorization method for learning attribute representations"
    - "collaborative deep learning for recommender systems"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-4 (induced by source paper "cost effective object detection active sample mining with switchable selection criteria"):
  with members:
    - "few example object detection with model communication"
    - "active self paced learning for cost effective and progressive face identification"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "cost effective active learning for deep image classification"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "automatic subspace learning via principal coefficients embedding"
    - "weakly and semi supervised object detection with expectation maximization algorithm"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"):
  with members:
    - "noisy subspace clustering via matching pursuits"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "sssc am a unified framework for video co segmentation by structured sparse subspace clustering with appearance and motion features"
    - "convex sparse spectral clustering single view to multi view"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Connected through hyperedge HE-6 (induced by source paper "automatic subspace learning via principal coefficients embedding"):
  with members:
    - "multi view intact space learning"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "fixed rank representation for unsupervised visual learning"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust principal component analysis"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-7 (induced by source paper "background subtraction via fast robust matrix completion"):
  with members:
    - "automatic subspace learning via principal coefficients embedding"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "kernel truncated regression representation for robust subspace clustering"):
  with members:
    - "correntropy induced l2 graph for robust subspace clustering"
    - "locally linear representation for image clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

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)