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: "learning converged propagations with deep prior ensemble for image enhancement"
  Abstract: Enhancing the visual qualities of images plays very important roles in various vision and learning applications. In the past few years, both knowledge-driven maximum a posterior (MAP) with prior modelings and fully data-dependent convolutional neural network (CNN) techniques have been investigated to address specific enhancement tasks. In this paper, by exploiting the advantages of these two types of mechanisms within a complementary propagation perspective, we propose a unified framework, named deep prior ensemble (DPE) for solving various image enhancement tasks. Specifically, we first establish the basic propagation scheme based on the fundamental image modeling cues and then introduce residual CNNs to help predicting the propagation direction at each stage. By designing prior projections to perform feedback control, we theoretically prove that even with experience-inspired CNNs, DPE is definitely converged and the output will always satisfy our fundamental task constraints. The main advantage against the conventional optimization-based MAP approaches is that our descent directions are learned from collected training data, thus are much more robust to unwanted local minimums. While, compared with existing CNN type networks, which are often designed in heuristic manners without theoretical guarantees, DPE is able to gain advantages from rich task cues investigated on the bases of domain knowledges. Therefore, the DPE actually provides a generic ensemble methodology to integrate both knowledge and data-based cues for different image enhancement tasks. More importantly, our theoretical investigations verify that the feed-forward propagations of DPE are properly controlled toward our desired solution. Experimental results demonstrate that the proposed DPE outperforms the state-of-the-arts on a variety of image enhancement tasks in terms of both quantitative measure and visual perception quality.

Connected through hyperedge HE-1 (induced by source paper "deep plug and play super resolution for arbitrary blur kernels"):
  with members:
    - "gated fusion network for joint image deblurring and super resolution"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "regularization by denoising clarifications and new interpretations"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "neural nearest neighbors networks"
    - "deep cnn denoiser and multi layer neighbor component embedding for face hallucination"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-2 (induced by source paper "neural imaging pipelines the scourge or hope of forensics"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "stereoscopic dark flash for low light photography"
    - "cnn based adversarial embedding for image steganography"
    - "learning rich features for image manipulation detection"
    - "synthetic depth of field with a single camera mobile phone"
    - "do gans leave artificial fingerprints"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "content authentication for neural imaging pipelines end to end optimization of photo provenance in complex distribution channels"

Connected through hyperedge HE-3 (induced by source paper "a deep optimization approach for image deconvolution"):
  with members:
    - "learning an optimizer for image deconvolution"
    - "scale recurrent network for deep image deblurring"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "learning a discriminative prior for blind image deblurring"
    - "blind image deconvolution using deep generative priors"
    - "learning collaborative generation correction modules for blind image deblurring and beyond"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "motion deblurring in the wild"

Connected through hyperedge HE-4 (induced by source paper "controlling neural networks via energy dissipation"):
  with members:
    - "linear inequality constraints for neural network activations"
    - "homogeneous linear inequality constraints for neural network activations"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "scale recurrent network for deep image deblurring"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "deep image prior"
    - "recurrent relational networks"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"

Connected through hyperedge HE-5 (induced by source paper "bilevel integrative optimization for ill posed inverse problems"):
  with members:
    - "end to end learning of motion representation for video understanding"
    - "fast and accurate single image super resolution via information distillation network"
    - "on the convergence of learning based iterative methods for nonconvex inverse problems"
    - "adversarial regularizers in inverse problems"
    - "non local recurrent network for image restoration"
    - "a theoretically guaranteed deep optimization framework for robust compressive sensing mri"
    - "deep back projection networks for super resolution"
    - "non local neural networks"

Connected through hyperedge HE-6 (induced by source paper "underexposed image correction via hybrid priors navigated deep propagation"):
  with members:
    - "kindling the darkness a practical low light image enhancer"
    - "on the convergence of learning based iterative methods for nonconvex inverse problems"
    - "yolov3 an incremental improvement"
    - "deep retinex decomposition for low light enhancement"
    - "residual dense network for image super resolution"
    - "learning collaborative generation correction modules for blind image deblurring and beyond"
    - "deep image prior"
    - "deep bilateral learning for real time image enhancement"

Connected through hyperedge HE-7 (induced by source paper "dense haze a benchmark for image dehazing with dense haze and haze free images"):
  with members:
    - "o haze a dehazing benchmark with real hazy and haze free outdoor images"
    - "dehazenet an end to end system for single image haze removal"

Connected through hyperedge HE-8 (induced by source paper "task embedded coordinate update a realizable framework for multivariate non convex optimization"):
  with members:
    - "fast low rank shared dictionary learning for image classification"
    - "deep bilateral learning for real time image enhancement"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "optimization with sparsity inducing penalties"

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)