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 and robust archetypal analysis for representation learning"
  Abstract: We revisit a pioneer unsupervised learning technique called archetypal analysis, which is related to successful data analysis methods such as sparse coding and non-negative matrix factorization. Since it was proposed, archetypal analysis did not gain a lot of popularity even though it produces more interpretable models than other alternatives. Because no efficient implementation has ever been made publicly available, its application to important scientific problems may have been severely limited. Our goal is to bring back into favour archetypal analysis. We propose a fast optimization scheme using an active-set strategy, and provide an efficient open-source implementation interfaced with Matlab, R, and Python. Then, we demonstrate the usefulness of archetypal analysis for computer vision tasks, such as codebook learning, signal classification, and large image collection visualization.

Connected through hyperedge HE-1 (induced by source paper "discrete laplace operator estimation for dynamic 3d reconstruction"):
  with members:
    - "deep graph laplacian regularization for robust denoising of real images"
    - "automatic adaptation of person association for multiview tracking in group activities"
    - "learning monocular 3d human pose estimation from multi view images"
    - "convolutional pose machines"
    - "panoptic studio a massively multiview system for social interaction capture"
    - "self expressive dictionary learning for dynamic 3d reconstruction"

Connected through hyperedge HE-2 (induced by source paper "finding archetypal spaces for data using neural networks"):
  with members:
    - "geometry based data generation"

Connected through hyperedge HE-3 (induced by source paper "self expressive dictionary learning for dynamic 3d reconstruction"):
  with members:
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-4 (induced by source paper "sparse modeling for image and vision processing"):
  with members:
    - "sparsity based poisson denoising with dictionary learning"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "generalized max pooling"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "invariant scattering convolution networks"
    - "sparse modeling of intrinsic correspondences"

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)