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 dictionary learning acceleration and convergence"
  Abstract: Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems. To moderate these problems, this paper proposes a new practically feasible and convergent  Block Proximal Gradient method using a Majorizer  (BPG-M) for CDL. The BPG-M-based CDL is investigated with different block updating schemes and majorization matrix designs, and further accelerated by incorporating some momentum coefficient formulas and restarting techniques. All of the methods investigated incorporate a boundary artifacts removal (or, more generally, sampling) operator in the learning model. Numerical experiments show that, without needing any parameter tuning process, the proposed BPG-M approach converges more stably to desirable solutions of lower objective values than the existing state-of-the-art ADMM algorithm and its memory-efficient variant do. Compared with the ADMM approaches, the BPG-M method using a multi-block updating scheme is particularly useful in single-threaded CDL algorithm handling large data sets, due to its lower memory requirement and no polynomial computational complexity. Image denoising experiments show that, for relatively strong additive white Gaussian noise, the filters learned by BPG-M-based CDL outperform those trained by the ADMM approach.

Connected through hyperedge HE-1 (induced by source paper "convolutional dictionary pair learning network for image representation learning"):
  with members:
    - "majorization minimization technique for optimally solving deep dictionary learning"
    - "multilayer collaborative low rank coding network for robust deep subspace discovery"
    - "robust subspace clustering with compressed data"
    - "learning structured twin incoherent twin projective latent dictionary pairs for classification"
    - "scalable block diagonal locality constrained projective dictionary learning"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "convolutional analysis operator learning dependence on training data"):
  with members:
    - "learning co sparse analysis operators with separable structures"
    - "analysis operator learning and its application to image reconstruction"

Connected through hyperedge HE-3 (induced by source paper "deep micro dictionary learning and coding network"):
  with members:
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "structure aware classification using supervised dictionary learning"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "greedy deep dictionary learning"
    - "discriminative bayesian dictionary learning for classification"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "convolutional clustering for unsupervised learning"
    - "pcanet a simple deep learning baseline for image classification"

Connected through hyperedge HE-4 (induced by source paper "convolutional dictionary learning a comparative review and new algorithms"):
  with members:
    - "first and second order methods for online convolutional dictionary learning"
    - "synthesis versus analysis in patch based image priors"
    - "online convolutional dictionary learning for multimodal imaging"
    - "online convolutional dictionary learning"
    - "convolutional sparse coding for high dynamic range imaging"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-5 (induced by source paper "online convolutional dictionary learning"):
  with members:
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "convolutional dictionary learning via local processing"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "online convolutional dictionary learning for multimodal imaging"
    - "scalable online convolutional sparse coding"
    - "a survey of sparse representation algorithms and applications"
    - "a fast patch dictionary method for whole image recovery"
    - "sparse modeling for image and vision processing"

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.LG(Machine Learning)