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: "pirm challenge on perceptual image enhancement on smartphones report"
  Abstract: This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tracks. In the first one, participants were solving the classical image super-resolution problem with a bicubic downscaling factor of 4. The second track was aimed at real-world photo enhancement, and the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with a DSLR camera. The target metric used in this challenge combined the runtime, PSNR scores and solutions' perceptual results measured in the user study. To ensure the efficiency of the submitted models, we additionally measured their runtime and memory requirements on Android smartphones. The proposed solutions significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.

Connected through hyperedge HE-1 (induced by source paper "ddet dual path dynamic enhancement network for real world image super resolution"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "lightweight feature fusion network for single image super resolution"
    - "super resolution via image adapted denoising cnns incorporating external and internal learning"
    - "attention aware face hallucination via deep reinforcement learning"
    - "structure preserving image super resolution via contextualized multitask learning"
    - "enhanced deep residual networks for single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "dynamic filter networks"

Connected through hyperedge HE-2 (induced by source paper "ai benchmark all about deep learning on smartphones in 2019"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "on device neural net inference with mobile gpus"
    - "semantic image synthesis with spatially adaptive normalization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "synthetic depth of field with a single camera mobile phone"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ai benchmark running deep neural networks on android smartphones"
    - "relaxed quantization for discretized neural networks"

Connected through hyperedge HE-3 (induced by source paper "mobisr efficient on device super resolution through heterogeneous mobile processors"):
  with members:
    - "embench quantifying performance variations of deep neural networks across modern commodity devices"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "deploying deep neural networks in the embedded space"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fast and accurate single image super resolution via information distillation network"
    - "effnet an efficient structure for convolutional neural networks"
    - "image super resolution using very deep residual channel attention networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-4 (induced by source paper "difficulty aware image super resolution via deep adaptive dual network"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "fast and accurate single image super resolution via information distillation network"
    - "image super resolution using very deep residual channel attention networks"
    - "multi level wavelet cnn for image restoration"
    - "attention aware face hallucination via deep reinforcement learning"
    - "structure preserving image super resolution via contextualized multitask learning"
    - "enhanced deep residual networks for single image super resolution"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-5 (induced by source paper "on device neural net inference with mobile gpus"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-6 (induced by source paper "ai benchmark running deep neural networks on android smartphones"):
  with members:
    - "a quantization friendly separable convolution for mobilenets"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "wespe weakly supervised photo enhancer for digital cameras"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "icnet for real time semantic segmentation on high resolution images"
    - "a deep reinforcement learning chatbot"
    - "cnndroid gpu accelerated execution of trained deep convolutional neural networks on android"

Connected through hyperedge HE-7 (induced by source paper "fast perceptual image enhancement"):
  with members:
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "wespe weakly supervised photo enhancer for digital cameras"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "dehazenet an end to end system for single image haze removal"

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)