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: "toward designing convergent deep operator splitting methods for task specific nonconvex optimization"
  Abstract: Operator splitting methods have been successfully used in computational sciences, statistics, learning and vision areas to reduce complex problems into a series of simpler subproblems. However, prevalent splitting schemes are mostly established only based on the mathematical properties of some general optimization models. So it is a laborious process and often requires many iterations of ideation and validation to obtain practical and task-specific optimal solutions, especially for nonconvex problems in real-world scenarios. To break through the above limits, we introduce a new algorithmic framework, called Learnable Bregman Splitting (LBS), to perform deep-architecture-based operator splitting for nonconvex optimization based on specific task model. Thanks to the data-dependent (i.e., learnable) nature, our LBS can not only speed up the convergence, but also avoid unwanted trivial solutions for real-world tasks. Though with inexact deep iterations, we can still establish the global convergence and estimate the asymptotic convergence rate of LBS only by enforcing some fairly loose assumptions. Extensive experiments on different applications (e.g., image completion and deblurring) verify our theoretical results and show the superiority of LBS against existing methods.

Connected through hyperedge HE-1 (induced by source paper "on the convergence of admm with task adaption and beyond"):
  with members:
    - "fastderain a novel video rain streak removal method using directional gradient priors"
    - "regularization by denoising clarifications and new interpretations"
    - "on the convergence of learning based iterative methods for nonconvex inverse problems"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "density aware single image de raining using a multi stream dense network"
    - "a theoretically guaranteed deep optimization framework for robust compressive sensing mri"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "proximal alternating direction network a globally converged deep unrolling framework"

Connected through hyperedge HE-2 (induced by source paper "learning collaborative generation correction modules for blind image deblurring and beyond"):
  with members:
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "proximal alternating direction network a globally converged deep unrolling framework"
    - "learning deep cnn denoiser prior for image restoration"
    - "learning fully convolutional networks for iterative non blind deconvolution"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "cascades of regression tree fields for image restoration"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-3 (induced by source paper "learning aggregated transmission propagation networks for haze removal and beyond"):
  with members:
    - "proximal alternating direction network a globally converged deep unrolling framework"
    - "dehazenet an end to end system for single image haze removal"
    - "identity mappings in deep residual networks"
    - "energy based generative adversarial network"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "haze visibility enhancement a survey and quantitative benchmarking"
    - "cascades of regression tree fields for image restoration"
    - "deep residual learning for image recognition"

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)