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: "physics based generative adversarial models for image restoration and beyond"
  Abstract: We present an algorithm to directly solve numerous image restoration problems (e.g., image deblurring, image dehazing, image deraining, etc.). These problems are highly ill-posed, and the common assumptions for existing methods are usually based on heuristic image priors. In this paper, we find that these problems can be solved by generative models with adversarial learning. However, the basic formulation of generative adversarial networks (GANs) does not generate realistic images, and some structures of the estimated images are usually not preserved well. Motivated by an interesting observation that the estimated results should be consistent with the observed inputs under the physics models, we propose a physics model constrained learning algorithm so that it can guide the estimation of the specific task in the conventional GAN framework. The proposed algorithm is trained in an end-to-end fashion and can be applied to a variety of image restoration and related low-level vision problems. Extensive experiments demonstrate that our method performs favorably against the state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "physics guided machine learning for scientific discovery an application in simulating lake temperature profiles"):
  with members:
    - "heavy rain image restoration integrating physics model and conditional adversarial learning"
    - "a domain guided cnn architecture for predicting age from structural brain images"
    - "hidden fluid mechanics a navier stokes informed deep learning framework for assimilating flow visualization data"
    - "physics guided neural networks pgnn an application in lake temperature modeling"
    - "label free supervision of neural networks with physics and domain knowledge"

Connected through hyperedge HE-2 (induced by source paper "deep likelihood network for image restoration with multiple degradations"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "the perception distortion tradeoff"
    - "on the learning dynamics of deep neural networks"
    - "generative image inpainting with contextual attention"
    - "generative face completion"
    - "deep image prior"
    - "learning a single convolutional super resolution network for multiple degradations"
    - "convolutional dictionary learning via local processing"

Connected through hyperedge HE-3 (induced by source paper "blending diverse physical priors with neural networks"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "neural illumination lighting prediction for indoor environments"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "block coordinate regularization by denoising"
    - "physics based neural networks for shape from polarization"
    - "heavy rain image restoration integrating physics model and conditional adversarial learning"
    - "neural lander stable drone landing control using learned dynamics"
    - "deep learning approach for fourier ptychography microscopy"

Connected through hyperedge HE-4 (induced by source paper "physics based neural networks for shape from polarization"):
  with members:
    - "neural lander stable drone landing control using learned dynamics"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "inverse transport networks"
    - "learning a discriminative prior for blind image deblurring"
    - "materials for masses svbrdf acquisition with a single mobile phone image"
    - "cnn ps cnn based photometric stereo for general non convex surfaces"
    - "single image svbrdf capture with a rendering aware deep network"
    - "flash photography for data driven hidden scene recovery"

Connected through hyperedge HE-5 (induced by source paper "blind motion deblurring with cycle generative adversarial networks"):
  with members:
    - "blind image deblurring using row column sparse representations"
    - "toward convolutional blind denoising of real photographs"
    - "learning blind motion deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "learning deep cnn denoiser prior for image restoration"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "an effective two branch model based deep network for single image deraining"):
  with members:
    - "spatial attentive single image deraining with a high quality real rain dataset"
    - "single image deraining a comprehensive benchmark analysis"
    - "heavy rain image restoration integrating physics model and conditional adversarial learning"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "fast single image rain removal via a deep decomposition composition network"
    - "single image deraining using scale aware multi stage recurrent network"
    - "clearing the skies a deep network architecture for single image rain removal"

Connected through hyperedge HE-7 (induced by source paper "automating motion correction in multishot mri using generative adversarial networks"):
  with members:
    - "u net convolutional networks for biomedical image segmentation"

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)