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: "exposure a white box photo post processing framework"
  Abstract: Retouching can significantly elevate the visual appeal of photos, but many casual photographers lack the expertise to do this well. To address this problem, previous works have proposed automatic retouching systems based on supervised learning from paired training images acquired before and after manual editing. As it is difficult for users to acquire paired images that reflect their retouching preferences, we present in this paper a deep learning approach that is instead trained on unpaired data, namely a set of photographs that exhibits a retouching style the user likes, which is much easier to collect. Our system is formulated using deep convolutional neural networks that learn to apply different retouching operations on an input image. Network training with respect to various types of edits is enabled by modeling these retouching operations in a unified manner as resolution-independent differentiable filters. To apply the filters in a proper sequence and with suitable parameters, we employ a deep reinforcement learning approach that learns to make decisions on what action to take next, given the current state of the image. In contrast to many deep learning systems, ours provides users with an understandable solution in the form of conventional retouching edits, rather than just a "black-box" result. Through quantitative comparisons and user studies, we show that this technique generates retouching results consistent with the provided photo set.

Connected through hyperedge HE-1 (induced by source paper "a differentiable color filter for generating unrestricted adversarial images"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "colorfool semantic adversarial colorization"
    - "functional adversarial attacks"
    - "a general framework for adversarial examples with objectives"
    - "panoptic feature pyramid networks"
    - "spatially transformed adversarial examples"
    - "semantic adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "deepflash turning a flash selfie into a studio portrait"):
  with members:
    - "angiodysplasia detection and localization using deep convolutional neural networks"
    - "single image reflection separation with perceptual losses"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "learning to see in the dark"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "deep bilateral learning for real time image enhancement"

Connected through hyperedge HE-3 (induced by source paper "revisiting image aesthetic assessment via self supervised feature learning"):
  with members:
    - "effective aesthetics prediction with multi level spatially pooled features"
    - "high fidelity image generation with fewer labels"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "unsupervised representation learning by predicting image rotations"
    - "aesthetic based clothing recommendation"
    - "tracking emerges by colorizing videos"
    - "predicting aesthetic score distribution through cumulative jensen shannon divergence"

Connected through hyperedge HE-4 (induced by source paper "unpaired image enhancement featuring reinforcement learning controlled image editing software"):
  with members:
    - "facelet bank for fast portrait manipulation"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "attention aware face hallucination via deep reinforcement learning"
    - "deep bilateral learning for real time image enhancement"

Connected through hyperedge HE-5 (induced by source paper "inversynth deep estimation of synthesizer parameter configurations from audio signals"):
  with members:
    - "neural network gradient based learning of black box function interfaces"
    - "deep synthesizer parameter estimation"
    - "bayesian neural word embedding"
    - "automatic tagging using deep convolutional neural networks"
    - "wavenet a generative model for raw audio"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-6 (induced by source paper "supervised and unsupervised learning of parameterized color enhancement"):
  with members:
    - "deep bilateral learning for real time image enhancement"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "automatic content aware color and tone stylization"
    - "adam a method for stochastic optimization"
    - "automatic photo adjustment using deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "dual illumination estimation for robust exposure correction"):
  with members:
    - "learning intrinsic image decomposition from watching the world"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "deep bilateral learning for real time image enhancement"
    - "automatic photo adjustment using deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "topologically guided color image enhancement"):
  with members:
    - "propagate and pair a single pass approach to critical point pairing in reeb graphs"
    - "the shape of an image a study of mapper on images"
    - "image segmentation using hierarchical merge tree"

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.GR(Graphics)