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: "hdr image reconstruction from a single exposure using deep cnns"
  Abstract: Camera sensors can only capture a limited range of luminance simultaneously, and in order to create high dynamic range (HDR) images a set of different exposures are typically combined. In this paper we address the problem of predicting information that have been lost in saturated image areas, in order to enable HDR reconstruction from a single exposure. We show that this problem is well-suited for deep learning algorithms, and propose a deep convolutional neural network (CNN) that is specifically designed taking into account the challenges in predicting HDR values. To train the CNN we gather a large dataset of HDR images, which we augment by simulating sensor saturation for a range of cameras. To further boost robustness, we pre-train the CNN on a simulated HDR dataset created from a subset of the MIT Places database. We demonstrate that our approach can reconstruct high-resolution visually convincing HDR results in a wide range of situations, and that it generalizes well to reconstruction of images captured with arbitrary and low-end cameras that use unknown camera response functions and post-processing. Furthermore, we compare to existing methods for HDR expansion, and show high quality results also for image based lighting. Finally, we evaluate the results in a subjective experiment performed on an HDR display. This shows that the reconstructed HDR images are visually convincing, with large improvements as compared to existing methods.

Connected through hyperedge HE-1 (induced by source paper "lighthouse predicting lighting volumes for spatially coherent illumination"):
  with members:
    - "neural illumination lighting prediction for indoor environments"
    - "fast spatially varying indoor lighting estimation"
    - "neural inverse rendering of an indoor scene from a single image"
    - "inverse rendering for complex indoor scenes shape spatially varying lighting and svbrdf from a single image"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "neural volumes learning dynamic renderable volumes from images"
    - "deeplight learning illumination for unconstrained mobile mixed reality"
    - "semantic image synthesis with spatially adaptive normalization"

Connected through hyperedge HE-2 (induced by source paper "the mertens unrolled network mu net a high dynamic range fusion neural network for through the windshield driver recognition"):
  with members:
    - "high dynamic range image forensics using cnn"
    - "retinaface single stage dense face localisation in the wild"
    - "arcface additive angular margin loss for deep face recognition"
    - "expandnet a deep convolutional neural network for high dynamic range expansion from low dynamic range content"
    - "recurrent inference machines for solving inverse problems"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "separating reflection and transmission images in the wild"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-3 (induced by source paper "synthetic defocus and look ahead autofocus for casual videography"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "deeply supervised salient object detection with short connections"
    - "synthetic depth of field with a single camera mobile phone"
    - "deeplens shallow depth of field from a single image"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "scanner efficient video analysis at scale"
    - "tracking emerges by colorizing videos"

Connected through hyperedge HE-4 (induced by source paper "neural imaging pipelines the scourge or hope of forensics"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "stereoscopic dark flash for low light photography"
    - "cnn based adversarial embedding for image steganography"
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "learning rich features for image manipulation detection"
    - "synthetic depth of field with a single camera mobile phone"
    - "do gans leave artificial fingerprints"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"

Connected through hyperedge HE-5 (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"
    - "deep bilateral learning for real time image enhancement"
    - "linknet exploiting encoder representations for efficient semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "single frame regularization for temporally stable cnns"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "learning blind video temporal consistency"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "exploring the landscape of spatial robustness"
    - "mixup beyond empirical risk minimization"
    - "characterizing and improving stability in neural style transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "detail revealing deep video super resolution"

Connected through hyperedge HE-7 (induced by source paper "illumination adaptive person re identification"):
  with members:
    - "unsupervised domain specific deblurring via disentangled representations"
    - "exploring disentangled feature representation beyond face identification"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "learning to see in the dark"
    - "image to image translation for cross domain disentanglement"
    - "drit diverse image to image translation via disentangled representations"
    - "pose driven deep convolutional model for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-8 (induced by source paper "neural illumination lighting prediction for indoor environments"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "geometry aware deep network for single image novel view synthesis"
    - "learning to estimate indoor lighting from 3d objects"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "learning to predict indoor illumination from a single image"
    - "matterport3d learning from rgb d data in indoor environments"
    - "what is around the camera"

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)