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: "deep lambertian networks"
  Abstract: Visual perception is a challenging problem in part due to illumination variations. A possible solution is to first estimate an illumination invariant representation before using it for recognition. The object albedo and surface normals are examples of such representations. In this paper, we introduce a multilayer generative model where the latent variables include the albedo, surface normals, and the light source. Combining Deep Belief Nets with the Lambertian reflectance assumption, our model can learn good priors over the albedo from 2D images. Illumination variations can be explained by changing only the lighting latent variable in our model. By transferring learned knowledge from similar objects, albedo and surface normals estimation from a single image is possible in our model. Experiments demonstrate that our model is able to generalize as well as improve over standard baselines in one-shot face recognition.

Connected through hyperedge HE-1 (induced by source paper "temporal transformer networks joint learning of invariant and discriminative time warping"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "can recurrent neural networks warp time"
    - "learning to exploit invariances in clinical time series data using sequence transformer networks"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "dynamic routing between capsules"
    - "learning invariant riemannian geometric representations using deep nets"
    - "graph based isometry invariant representation learning"
    - "a new representation of skeleton sequences for 3d action recognition"

Connected through hyperedge HE-2 (induced by source paper "learning to separate multiple illuminants in a single image"):
  with members:
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning intrinsic image decomposition from watching the world"
    - "materials for masses svbrdf acquisition with a single mobile phone image"
    - "learning to predict indoor illumination from a single image"
    - "illuminant spectra based source separation using flash photography"
    - "semantic scene completion from a single depth image"
    - "perceptual losses for real time style transfer and super resolution"
    - "deep outdoor illumination estimation"

Connected through hyperedge HE-3 (induced by source paper "inverse transport networks"):
  with members:
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "material editing using a physically based rendering network"
    - "neural 3d mesh renderer"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "the stan math library reverse mode automatic differentiation in c"
    - "adam a method for stochastic optimization"
    - "designing deep networks for surface normal estimation"

Connected through hyperedge HE-4 (induced by source paper "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "real time 3d face fitting and texture fusion on in the wild videos"
    - "face alignment in the wild a survey"
    - "facial performance capture with deep neural networks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "production level facial performance capture using deep convolutional neural networks"
    - "deep cascaded bi network for face hallucination"
    - "robust lstm autoencoders for face de occlusion in the wild"

Connected through hyperedge HE-5 (induced by source paper "modeling surface appearance from a single photograph using self augmented convolutional neural networks"):
  with members:
    - "virtual worlds as proxy for multi object tracking analysis"
    - "coupled generative adversarial networks"
    - "synthetic data for text localisation in natural images"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep reflectance maps"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "material editing using a physically based rendering network"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "delight net decomposing reflectance maps into specular materials and natural illumination"
    - "learning data driven reflectance priors for intrinsic image decomposition"
    - "shapenet an information rich 3d model repository"
    - "deep convolutional inverse graphics network"
    - "deep reflectance maps"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"

Connected through hyperedge HE-7 (induced by source paper "joint material and illumination estimation from photo sets in the wild"):
  with members:
    - "live intrinsic material estimation"
    - "material editing using a physically based rendering network"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "novel views of objects from a single image"
    - "natural illumination from multiple materials using deep learning"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "designing deep networks for surface normal estimation"

Connected through hyperedge HE-8 (induced by source paper "self supervised intrinsic image decomposition"):
  with members:
    - "deep shading convolutional neural networks for screen space shading"
    - "marrnet 3d shape reconstruction via 2 5d sketches"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "shapenet an information rich 3d model repository"
    - "deep convolutional inverse graphics network"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "adam a method for stochastic optimization"

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)