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: "convolutional matching pursuit and dictionary training"
  Abstract: Here, {W,Z} are the dictionary and the coefficients, respectively, and zk is the kth column of Z. K, q, and λ are user selected parameters controlling the power of the model. More recently, many models with additional structure have been proposed. For example, in [9, 2], the dictionary elements are arranged in groups and the sparsity is on the group level. In [3, 5, 7], the dictionaries are constructed to be translation invariant. In the former work, the dictionary is constructed via a non-negative matrix factorization. In the latter two works, the construction is a convolutional analogue of 1.2 or an l variant, with 0 < p < 1. In this short note we work with greedy algorithms for solving the convolutional analogues of 1.1. Specifically, we demonstrate that sparse coding by matching pursuit and dictionary learning via K-SVD [1] can be used in the translation invariant setting.

Connected through hyperedge HE-1 (induced by source paper "effect of top down connections in hierarchical sparse coding"):
  with members:
    - "deep predictive coding network for object recognition"
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "deep predictive coding network with local recurrent processing for object recognition"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "winner take all autoencoders"
    - "k sparse autoencoders"

Connected through hyperedge HE-2 (induced by source paper "sparse deep predictive coding captures contour integration capabilities of the early visual system"):
  with members:
    - "deep predictive coding network for object recognition"
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "adam a method for stochastic optimization"
    - "k sparse autoencoders"

Connected through hyperedge HE-3 (induced by source paper "analysis dictionary learning an efficient and discriminative solution"):
  with members:
    - "analysis dictionary learning based classification structure for robustness"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "structured analysis dictionary learning for image classification"
    - "pcanet a simple deep learning baseline for image classification"

Connected through hyperedge HE-4 (induced by source paper "multilayer convolutional sparse modeling pursuit and dictionary learning"):
  with members:
    - "convolutional dictionary learning via local processing"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "online convolutional dictionary learning"
    - "trainlets dictionary learning in high dimensions"
    - "maximal sparsity with deep networks"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "on the expressive power of deep learning a tensor analysis"
    - "winner take all autoencoders"

Connected through hyperedge HE-5 (induced by source paper "convolutional sparse coding for capturing high speed video content"):
  with members:
    - "convolutional sparse coding for high dynamic range imaging"
    - "optimization methods for convolutional sparse coding"
    - "compressed compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "working locally thinking globally theoretical guarantees for convolutional sparse coding"):
  with members:
    - "on the global local dichotomy in sparsity modeling"
    - "convolutional dictionary learning through tensor factorization"
    - "boosting of image denoising algorithms"
    - "optimization methods for convolutional sparse coding"
    - "sparse modeling for image and vision processing"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-7 (induced by source paper "convolutional sparse coding for high dynamic range imaging"):
  with members:
    - "optimization methods for convolutional sparse coding"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

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)