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: "heavy rain image restoration integrating physics model and conditional adversarial learning"
  Abstract: Most deraining works focus on rain streaks removal but they cannot deal adequately with heavy rain images. In heavy rain, streaks are strongly visible, dense rain accumulation or rain veiling effect significantly washes out the image, further scenes are relatively more blurry, etc. In this paper, we propose a novel method to address these problems. We put forth a 2-stage network: a physics-based backbone followed by a depth-guided GAN refinement. The first stage estimates the rain streaks, the transmission, and the atmospheric light governed by the underlying physics. To tease out these components more reliably, a guided filtering framework is used to decompose the image into its low- and high-frequency components. This filtering is guided by a rain-free residue image --- its content is used to set the passbands for the two channels in a spatially-variant manner so that the background details do not get mixed up with the rain-streaks. For the second stage, the refinement stage, we put forth a depth-guided GAN to recover the background details failed to be retrieved by the first stage, as well as correcting artefacts introduced by that stage. We have evaluated our method against the state of the art methods. Extensive experiments show that our method outperforms them on real rain image data, recovering visually clean images with good details.

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:
    - "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 based generative adversarial models for image restoration and beyond"
    - "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 "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"
    - "neural lander stable drone landing control using learned dynamics"
    - "deep learning approach for fourier ptychography microscopy"
    - "learning to separate multiple illuminants in a single image"

Connected through hyperedge HE-3 (induced by source paper "visual physics discovering physical laws from videos"):
  with members:
    - "data driven partial derivative equations discovery with evolutionary approach"
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "physics based rendering for improving robustness to rain"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "neural guided symbolic regression with semantic prior"
    - "physics based neural networks for shape from polarization"
    - "neural lander stable drone landing control using learned dynamics"
    - "gp rvm genetic programing based symbolic regression using relevance vector machine"

Connected through hyperedge HE-4 (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"
    - "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"
    - "physics based generative adversarial models for image restoration and beyond"
    - "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-5 (induced by source paper "deep image deraining via intrinsic rainy image priors and multi scale auxiliary decoding"):
  with members:
    - "an effective two branch model based deep network for single image deraining"
    - "spatial attentive single image deraining with a high quality real rain dataset"
    - "progressive image deraining networks a better and simpler baseline"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "noise2noise learning image restoration without clean data"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "fast single image rain removal via a deep decomposition composition network"
    - "clearing the skies a deep network architecture for single image rain removal"

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)