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: "decouple learning for parameterized image operators"
  Abstract: Many different deep networks have been used to approximate, accelerate or improve traditional image operators, such as image smoothing, super-resolution and denoising. Among these traditional operators, many contain parameters which need to be tweaked to obtain the satisfactory results, which we refer to as "parameterized image operators". However, most existing deep networks trained for these operators are only designed for one specific parameter configuration, which does not meet the needs of real scenarios that usually require flexible parameters settings. To overcome this limitation, we propose a new decouple learning algorithm to learn from the operator parameters to dynamically adjust the weights of a deep network for image operators, denoted as the base network. The learned algorithm is formed as another network, namely the weight learning network, which can be end-to-end jointly trained with the base network. Experiments demonstrate that the proposed framework can be successfully applied to many traditional parameterized image operators. We provide more analysis to better understand the proposed framework, which may inspire more promising research in this direction. Our codes and models have been released in this https URL

Connected through hyperedge HE-1 (induced by source paper "meta sr a magnification arbitrary network for super resolution"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "metaanchor learning to detect objects with customized anchors"
    - "image super resolution using very deep residual channel attention networks"
    - "residual dense network for image super resolution"
    - "image super resolution via dual state recurrent networks"
    - "memory matching networks for one shot image recognition"
    - "learning a single convolutional super resolution network for multiple degradations"
    - "learning to segment every thing"

Connected through hyperedge HE-2 (induced by source paper "learning to discretize solving 1d scalar conservation laws via deep reinforcement learning"):
  with members:
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "solving irregular and data enriched differential equations using deep neural networks"
    - "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"
    - "neural ordinary differential equations"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "proximal policy optimization algorithms"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-3 (induced by source paper "tan temporal aggregation network for dense multi label action recognition"):
  with members:
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "stereoscopic neural style transfer"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "deep motion boundary detection"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what actions are needed for understanding human actions in videos"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-4 (induced by source paper "stroke controllable fast style transfer with adaptive receptive fields"):
  with members:
    - "stereoscopic neural style transfer"
    - "deep exemplar based colorization"
    - "context encoding for semantic segmentation"
    - "demystifying neural style transfer"
    - "multi style generative network for real time transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "deformable convolutional networks"
    - "style transfer via texture synthesis"

Connected through hyperedge HE-5 (induced by source paper "evolvement constrained adversarial learning for video style transfer"):
  with members:
    - "stereoscopic neural style transfer"
    - "deep exemplar based colorization"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stylebank an explicit representation for neural image style transfer"
    - "coherent online video style transfer"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-6 (induced by source paper "gated context aggregation network for image dehazing and deraining"):
  with members:
    - "gated fusion network for single image dehazing"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "density aware single image de raining using a multi stream dense network"
    - "deep exemplar based colorization"
    - "smoothed dilated convolutions for improved dense prediction"
    - "densely connected pyramid dehazing network"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "benchmarking single image dehazing and beyond"

Connected through hyperedge HE-7 (induced by source paper "emerging applications of reversible data hiding"):
  with members:
    - "stereoscopic neural style transfer"
    - "deep exemplar based colorization"
    - "stylebank an explicit representation for neural image style transfer"
    - "coherent online video style transfer"
    - "learning representations for automatic colorization"
    - "texture synthesis using convolutional neural networks"

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)