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: "image super resolution via dual state recurrent networks"
  Abstract: Advances in image super-resolution (SR) have recently benefited significantly from rapid developments in deep neural networks. Inspired by these recent discoveries, we note that many state-of-the-art deep SR architectures can be reformulated as a single-state recurrent neural network (RNN) with finite unfoldings. In this paper, we explore new structures for SR based on this compact RNN view, leading us to a dual-state design, the Dual-State Recurrent Network (DSRN). Compared to its single state counterparts that operate at a fixed spatial resolution, DSRN exploits both low-resolution (LR) and high-resolution (HR) signals jointly. Recurrent signals are exchanged between these states in both directions (both LR to HR and HR to LR) via delayed feedback. Extensive quantitative and qualitative evaluations on benchmark datasets and on a recent challenge demonstrate that the proposed DSRN performs favorably against state-of-the-art algorithms in terms of both memory consumption and predictive accuracy.

Connected through hyperedge HE-1 (induced by source paper "robust regression via deep negative correlation learning"):
  with members:
    - "residual non local attention networks for image restoration"
    - "bag of tricks for image classification with convolutional neural networks"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "residual dense network for image super resolution"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-2 (induced by source paper "learning to have an ear for face super resolution"):
  with members:
    - "seeing what a gan cannot generate"
    - "what makes training multi modal networks hard"
    - "toward real world single image super resolution a new benchmark and a new model"
    - "two stream action recognition oriented video super resolution"
    - "image super resolution by neural texture transfer"
    - "ranksrgan generative adversarial networks with ranker for image super resolution"
    - "evaluating robustness of deep image super resolution against adversarial attacks"
    - "zoom to learn learn to zoom"

Connected through hyperedge HE-3 (induced by source paper "feedback network for image super resolution"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "top down feedback for crowd counting convolutional neural network"
    - "a fully progressive approach to single image super resolution"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "learning a single convolutional super resolution network for multiple degradations"

Connected through hyperedge HE-4 (induced by source paper "unsupervised and unregistered hyperspectral image super resolution with mutual dirichlet net"):
  with members:
    - "unsupervised sparse dirichlet net for hyperspectral image super resolution"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast and accurate single image super resolution via information distillation network"
    - "deep back projection networks for super resolution"
    - "mine mutual information neural estimation"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "boosting the accuracy of multispectral image pansharpening by learning a deep residual network"

Connected through hyperedge HE-5 (induced by source paper "image super resolution by neural texture transfer"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "crossnet an end to end reference based super resolution network using cross scale warping"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "improved training of wasserstein gans"
    - "age progression regression by conditional adversarial autoencoder"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-6 (induced by source paper "scale wise convolution for image restoration"):
  with members:
    - "non local recurrent network for image restoration"
    - "wide activation for efficient and accurate image super resolution"
    - "sniper efficient multi scale training"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "dssd deconvolutional single shot detector"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "enhanced deep residual networks for single image super resolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-7 (induced by source paper "point cloud super resolution with adversarial residual graph networks"):
  with members:
    - "splatnet sparse lattice networks for point cloud processing"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "so net self organizing network for point cloud analysis"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "residual dense network for image super resolution"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "pointsift a sift like network module for 3d point cloud semantic segmentation"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-8 (induced by source paper "gated multiple feedback network for image super resolution"):
  with members:
    - "feedback network for image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "top down feedback for crowd counting convolutional neural network"
    - "non local recurrent network for image restoration"
    - "residual dense network for image super resolution"
    - "sketch based manga retrieval using manga109 dataset"
    - "enhanced deep residual networks for single image super resolution"

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)