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: "single image super resolution via a lightweight residual convolutional neural network"
  Abstract: Recent years have witnessed great success of convolutional neural network (CNN) for various problems both in low and high level visions. Especially noteworthy is the residual network which was originally proposed to handle high-level vision problems and enjoys several merits. This paper aims to extend the merits of residual network, such as skip connection induced fast training, for a typical low-level vision problem, i.e., single image super-resolution. In general, the two main challenges of existing deep CNN for supper-resolution lie in the gradient exploding/vanishing problem and large numbers of parameters or computational cost as CNN goes deeper. Correspondingly, the skip connections or identity mapping shortcuts are utilized to avoid gradient exploding/vanishing problem. In addition, the skip connections have naturally centered the activation which led to better performance. To tackle with the second problem, a lightweight CNN architecture which has carefully designed width, depth and skip connections was proposed. In particular, a strategy of gradually varying the shape of network has been proposed for residual network. Different residual architectures for image super-resolution have also been compared. Experimental results have demonstrated that the proposed CNN model can not only achieve state-of-the-art PSNR and SSIM results for single image super-resolution but also produce visually pleasant results. This paper has extended the mmm 2017 oral conference paper with a considerable new analyses and more experiments especially from the perspective of centering activations and ensemble behaviors of residual network.

Connected through hyperedge HE-1 (induced by source paper "h owan multi distorted image restoration with tensor 1x1 convolution"):
  with members:
    - "path restore learning network path selection for image restoration"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "attention based adaptive selection of operations for image restoration in the presence of unknown combined distortions"
    - "mutan multimodal tucker fusion for visual question answering"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-2 (induced by source paper "depth wise decomposition for accelerating separable convolutions in efficient convolutional neural networks"):
  with members:
    - "mobinet a mobile binary network for image classification"
    - "pack and detect fast object detection in videos using region of interest packing"
    - "feature selective anchor free module for single shot object detection"
    - "prediction tracking segmentation"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-3 (induced by source paper "prediction tracking segmentation"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "fast online object tracking and segmentation a unifying approach"
    - "towards a better match in siamese network based visual object tracker"

Connected through hyperedge HE-4 (induced by source paper "bounding box regression with uncertainty for accurate object detection"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "prediction tracking segmentation"
    - "learning 6 dof grasping and pick place using attention focus"
    - "cornernet detecting objects as paired keypoints"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "amc automl for model compression and acceleration on mobile devices"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-5 (induced by source paper "shift based primitives for efficient convolutional neural networks"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "sparsely connected convolutional networks"
    - "sparsely aggregated convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "shift a zero flop zero parameter alternative to spatial convolutions"
    - "keynote small neural nets are beautiful enabling embedded systems with small deep neural network architectures"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-6 (induced by source paper "an empirical analysis of deep audio visual models for speech recognition"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "the conversation deep audio visual speech enhancement"
    - "large scale visual speech recognition"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "end to end audiovisual speech recognition"
    - "mnasnet platform aware neural architecture search for mobile"
    - "3d convolutional neural networks for cross audio visual matching recognition"

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)