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: "cnn based learning using reflection and retinex models for intrinsic image decomposition"
  Abstract: Most of the traditional work on intrinsic image decomposition rely on deriving priors about scene characteristics. On the other hand, recent research use deep learning models as in-and-out black box and do not consider the well-established, traditional image formation process as the basis of their intrinsic learning process. As a consequence, although current deep learning approaches show superior performance when considering quantitative benchmark results, traditional approaches are still dominant in achieving high qualitative results. In this paper, the aim is to exploit the best of the two worlds. A method is proposed that (1) is empowered by deep learning capabilities, (2) considers a physics-based reflection model to steer the learning process, and (3) exploits the traditional approach to obtain intrinsic images by exploiting reflectance and shading gradient information. The proposed model is fast to compute and allows for the integration of all intrinsic components. To train the new model, an object centered large-scale datasets with intrinsic ground-truth images are created. The evaluation results demonstrate that the new model outperforms existing methods. Visual inspection shows that the image formation loss function augments color reproduction and the use of gradient information produces sharper edges.

Connected through hyperedge HE-1 (induced by source paper "shadingnet image intrinsics by fine grained shading decomposition"):
  with members:
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "learning intrinsic image decomposition from watching the world"
    - "joint learning of intrinsic images and semantic segmentation"
    - "unsupervised deep single image intrinsic decomposition using illumination varying image sequences"
    - "intrinsic image transformation via scale space decomposition"
    - "squeeze and excitation networks"
    - "self supervised intrinsic image decomposition"

Connected through hyperedge HE-2 (induced by source paper "intrinseqnet learning to estimate the reflectance from varying illumination"):
  with members:
    - "live intrinsic material estimation"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "learning intrinsic image decomposition from watching the world"
    - "cnn ps cnn based photometric stereo for general non convex surfaces"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "self supervised intrinsic image decomposition"
    - "learning data driven reflectance priors for intrinsic image decomposition"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "relighting humans occlusion aware inverse rendering for full body human images"):
  with members:
    - "detailed accurate human shape estimation from clothed 3d scan sequences"
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "learning to predict indoor illumination from a single image"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "neural face editing with intrinsic image disentangling"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "sfsnet learning shape reflectance and illuminance of faces in the wild"
    - "deep outdoor illumination estimation"

Connected through hyperedge HE-4 (induced by source paper "semantic guided single image reflection removal"):
  with members:
    - "single image reflection separation with perceptual losses"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "crrn multi scale guided concurrent reflection removal network"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "rethinking atrous convolution for semantic image segmentation"
    - "separating reflection and transmission images in the wild"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "robust optical flow estimation of double layer images under transparency or reflection"

Connected through hyperedge HE-5 (induced by source paper "leveraging multi view image sets for unsupervised intrinsic image decomposition and highlight separation"):
  with members:
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning intrinsic image decomposition from watching the world"
    - "faces as lighting probes via unsupervised deep highlight extraction"
    - "self supervised intrinsic image decomposition"
    - "shading annotations in the wild"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"

Connected through hyperedge HE-6 (induced by source paper "color inference from semantic labeling for person search in videos"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "macro micro adversarial network for human parsing"
    - "looking at outfit to parse clothing"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-7 (induced by source paper "inverserendernet learning single image inverse rendering"):
  with members:
    - "deep hybrid real and synthetic training for intrinsic decomposition"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "deep ordinal regression network for monocular depth estimation"
    - "learning intrinsic image decomposition from watching the world"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "megadepth learning single view depth prediction from internet photos"
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-8 (induced by source paper "joint learning of intrinsic images and semantic segmentation"):
  with members:
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "darn a deep adversial residual network for intrinsic image decomposition"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "joint semantic segmentation and depth estimation with deep convolutional networks"
    - "unified depth prediction and intrinsic image decomposition from a single image via joint convolutional neural fields"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)