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: "learning intrinsic image decomposition from watching the world"
  Abstract: Single-view intrinsic image decomposition is a highly ill-posed problem, and so a promising approach is to learn from large amounts of data. However, it is difficult to collect ground truth training data at scale for intrinsic images. In this paper, we explore a different approach to learning intrinsic images: observing image sequences over time depicting the same scene under changing illumination, and learning single-view decompositions that are consistent with these changes. This approach allows us to learn without ground truth decompositions, and to instead exploit information available from multiple images when training. Our trained model can then be applied at test time to single views. We describe a new learning framework based on this idea, including new loss functions that can be efficiently evaluated over entire sequences. While prior learning-based methods achieve good performance on specific benchmarks, we show that our approach generalizes well to several diverse datasets, including MIT intrinsic images, Intrinsic Images in the Wild and Shading Annotations in the Wild.

Connected through hyperedge HE-1 (induced by source paper "implicit generation and generalization in energy based models"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "maximum entropy generators for energy based models"
    - "the gan landscape losses architectures regularization and normalization"
    - "learning dexterous in hand manipulation"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "spectral normalization for generative adversarial networks"
    - "deep anomaly detection with outlier exposure"
    - "autoregressive quantile networks for generative modeling"

Connected through hyperedge HE-2 (induced by source paper "end to end time lapse video synthesis from a single outdoor image"):
  with members:
    - "video to video synthesis"
    - "recycle gan unsupervised video retargeting"
    - "a closed form solution to photorealistic image stylization"
    - "deep sets"
    - "night to day image translation for retrieval based localization"
    - "drit diverse image to image translation via disentangled representations"
    - "decomposing motion and content for natural video sequence prediction"
    - "neural color transfer between images"

Connected through hyperedge HE-3 (induced by source paper "argan attentive recurrent generative adversarial network for shadow detection and removal"):
  with members:
    - "dynamic meta embeddings for improved sentence representations"
    - "spectral normalization for generative adversarial networks"
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "direction aware spatial context features for shadow detection and removal"
    - "a d net training a shadow detector with adversarial shadow attenuation"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "direction aware spatial context features for shadow detection"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-4 (induced by source paper "neural inverse rendering of an indoor scene from a single image"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "deep ordinal regression network for monocular depth estimation"
    - "inverserendernet learning single image inverse rendering"
    - "inverse transport networks"
    - "materials for masses svbrdf acquisition with a single mobile phone image"
    - "learning to predict indoor illumination from a single image"

Connected through hyperedge HE-5 (induced by source paper "training image estimators without image ground truth"):
  with members:
    - "noise2noise learning image restoration without clean data"
    - "an unsupervised approach to solving inverse problems using generative adversarial networks"
    - "training deep learning based image denoisers from undersampled measurements without ground truth and without image prior"
    - "deep semantic face deblurring"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "unsupervised learning of depth and ego motion from video"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"

Connected through hyperedge HE-6 (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"
    - "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"
    - "cnn based learning using reflection and retinex models for intrinsic image decomposition"

Connected through hyperedge HE-7 (induced by source paper "separate in latent space unsupervised single image layer separation"):
  with members:
    - "face image reflection removal"
    - "unsupervised deep single image intrinsic decomposition using illumination varying image sequences"
    - "single image reflection separation with perceptual losses"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "self supervised intrinsic image decomposition"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "dualgan unsupervised dual learning for image to image translation"

Connected through hyperedge HE-8 (induced by source paper "unsupervised learning for intrinsic image decomposition from a single image"):
  with members:
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "multimodal unsupervised image to image translation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "unsupervised image to image translation networks"
    - "self supervised intrinsic image decomposition"
    - "shading annotations in the wild"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "instance normalization the missing ingredient for fast stylization"

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)