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: "feedback network for image super resolution"
  Abstract: Recent advances in image super-resolution (SR) explored the power of deep learning to achieve a better reconstruction performance. However, the feedback mechanism, which commonly exists in human visual system, has not been fully exploited in existing deep learning based image SR methods. In this paper, we propose an image super-resolution feedback network (SRFBN) to refine low-level representations with high-level information. Specifically, we use hidden states in an RNN with constraints to achieve such feedback manner. A feedback block is designed to handle the feedback connections and to generate powerful high-level representations. The proposed SRFBN comes with a strong early reconstruction ability and can create the final high-resolution image step by step. In addition, we introduce a curriculum learning strategy to make the network well suitable for more complicated tasks, where the low-resolution images are corrupted by multiple types of degradation. Extensive experimental results demonstrate the superiority of the proposed SRFBN in comparison with the state-of-the-art methods. Code is avaliable at this https URL.

Connected through hyperedge HE-1 (induced by source paper "feedback graph convolutional network for skeleton based action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "skeleton image representation for 3d action recognition based on tree structure and reference joints"
    - "interaction relational network for mutual action recognition"
    - "vertex feature encoding and hierarchical temporal modeling in a spatial temporal graph convolutional network for action recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "part based graph convolutional network for action recognition"

Connected through hyperedge HE-2 (induced by source paper "meta transfer learning for zero shot super resolution"):
  with members:
    - "blind super resolution with iterative kernel correction"
    - "singan learning a generative model from a single natural image"
    - "residual non local attention networks for image restoration"
    - "on first order meta learning algorithms"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-3 (induced by source paper "blurry video frame interpolation"):
  with members:
    - "unsupervised video interpolation using cycle consistency"
    - "depth aware video frame interpolation"
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "adversarial spatio temporal learning for video deblurring"
    - "temporal cycle consistency learning"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "video enhancement with task oriented flow"
    - "video to video synthesis"

Connected through hyperedge HE-4 (induced by source paper "simultaneous enhancement and super resolution of underwater imagery for improved visual perception"):
  with members:
    - "real time model based image color correction for underwater robots"
    - "deep learning for single image super resolution a brief review"
    - "fast underwater image enhancement for improved visual perception"
    - "an underwater image enhancement benchmark dataset and beyond"
    - "deep visual attention prediction"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "fast and accurate single image super resolution via information distillation network"
    - "esrgan enhanced super resolution generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "semantically aligned bias reducing zero shot learning"
    - "out of distribution detection for generalized zero shot action recognition"
    - "transferable contrastive network for generalized zero shot learning"
    - "generative dual adversarial network for generalized zero shot learning"
    - "towards universal representation for unseen action recognition"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "multi modal cycle consistent generalized zero shot learning"

Connected through hyperedge HE-6 (induced by source paper "differential dynamic programming neural optimizer"):
  with members:
    - "symplectic ode net learning hamiltonian dynamics with control"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "understanding and improving transformer from a multi particle dynamic system point of view"
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "deep learning theory review an optimal control and dynamical systems perspective"
    - "negative eigenvalues of the hessian in deep neural networks"
    - "hamiltonian neural networks"
    - "deep predictive coding network for object recognition"

Connected through hyperedge HE-7 (induced by source paper "self supervised image enhancement network training with low light images only"):
  with members:
    - "kindling the darkness a practical low light image enhancer"
    - "yolov3 an incremental improvement"
    - "toward convolutional blind denoising of real photographs"
    - "deep retinex decomposition for low light enhancement"
    - "learning to see in the dark"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "mask r cnn"

Connected through hyperedge HE-8 (induced by source paper "progressive perception oriented network for single image super resolution"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "maintaining natural image statistics with the contextual loss"
    - "fast and accurate single image super resolution via information distillation network"
    - "the relativistic discriminator a key element missing from standard gan"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "esrgan enhanced super resolution generative adversarial 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)