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: "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"
  Abstract: In this paper, we propose a new control framework called the moving endpoint control to restore images corrupted by different degradation levels in one model. The proposed control problem contains a restoration dynamics which is modeled by an RNN. The moving endpoint, which is essentially the terminal time of the associated dynamics, is determined by a policy network. We call the proposed model the dynamically unfolding recurrent restorer (DURR). Numerical experiments show that DURR is able to achieve state-of-the-art performances on blind image denoising and JPEG image deblocking. Furthermore, DURR can well generalize to images with higher degradation levels that are not included in the training stage.

Connected through hyperedge HE-1 (induced by source paper "understanding and improving transformer from a multi particle dynamic system point of view"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fixup initialization residual learning without normalization"
    - "universal transformers"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "scaling neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "nlh a blind pixel level non local method for real world image denoising"):
  with members:
    - "progressive image deraining networks a better and simpler baseline"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "simultaneous fidelity and regularization learning for image restoration"
    - "toward convolutional blind denoising of real photographs"
    - "neural nearest neighbors networks"
    - "real world noisy image denoising a new benchmark"
    - "noise2noise learning image restoration without clean data"

Connected through hyperedge HE-3 (induced by source paper "neural odes for image segmentation with level sets"):
  with members:
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "infocnf an efficient conditional continuous normalizing flow with adaptive solvers"
    - "waveglow a flow based generative network for speech synthesis"
    - "group normalization"
    - "collaborative annotation of semantic objects in images with multi granularity supervisions"
    - "neural ordinary differential equations"
    - "squeeze and excitation networks"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "deep learning via dynamical systems an approximation perspective"):
  with members:
    - "nonlinear approximation and deep relu networks"
    - "nonlinear approximation via compositions"
    - "barron spaces and the compositional function spaces for neural network models"
    - "understanding and improving transformer from a multi particle dynamic system point of view"
    - "a priori estimates of the population risk for residual networks"
    - "approximation capabilities of neural ordinary differential equations"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"
    - "monge amp ere flow for generative modeling"

Connected through hyperedge HE-5 (induced by source paper "disentangled image matting"):
  with members:
    - "dmcnn dual domain multi scale convolutional neural network for compression artifacts removal"
    - "alphagan generative adversarial networks for natural image matting"
    - "semantic human matting"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "deep image matting"
    - "designing effective inter pixel information flow for natural image matting"

Connected through hyperedge HE-6 (induced by source paper "localnorm robust image classification through dynamically regularized normalization"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "group normalization"
    - "noise2noise learning image restoration without clean data"
    - "differentiable learning to normalize via switchable normalization"
    - "generalisation in humans and deep neural networks"
    - "three mechanisms of weight decay regularization"
    - "don t decay the learning rate increase the batch size"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

Connected through hyperedge HE-7 (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"
    - "neural ordinary differential equations"
    - "decouple learning for parameterized image operators"
    - "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-8 (induced by source paper "kindling the darkness a practical low light image enhancer"):
  with members:
    - "deep retinex decomposition for low light enhancement"
    - "learning to see in the dark"
    - "msr net low light image enhancement using deep convolutional network"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a bio inspired multi exposure fusion framework for low light image enhancement"
    - "dehazenet an end to end system for single image haze removal"
    - "u net convolutional networks for biomedical image segmentation"

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)