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: "robust video content alignment and compensation for rain removal in a cnn framework"
  Abstract: Rain removal is important for improving the robustness of outdoor vision based systems. Current rain removal methods show limitations either for complex dynamic scenes shot from fast moving cameras, or under torrential rain fall with opaque occlusions. We propose a novel derain algorithm, which applies superpixel (SP) segmentation to decompose the scene into depth consistent units. Alignment of scene contents are done at the SP level, which proves to be robust towards rain occlusion and fast camera motion. Two alignment output tensors, i.e., optimal temporal match tensor and sorted spatial-temporal match tensor, provide informative clues for rain streak location and occluded background contents to generate an intermediate derain output. These tensors will be subsequently prepared as input features for a convolutional neural network to restore high frequency details to the intermediate output for compensation of mis-alignment blur. Extensive evaluations show that up to 5 dB reconstruction PSNR advantage is achieved over state-of-the-art methods. Visual inspection shows that much cleaner rain removal is achieved especially for highly dynamic scenes with heavy and opaque rainfall from a fast moving camera.

Connected through hyperedge HE-1 (induced by source paper "bridging the gap between computational photography and visual recognition"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "isaid a large scale dataset for instance segmentation in aerial images"
    - "report on ug 2 challenge track 1 assessing algorithms to improve video object detection and classification from unconstrained mobility platforms"
    - "single image deraining a comprehensive benchmark analysis"
    - "dual directed capsule network for very low resolution image recognition"
    - "vision meets drones a challenge"
    - "the perception distortion tradeoff"
    - "crafting a toolchain for image restoration by deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "spatial attentive single image deraining with a high quality real rain dataset"):
  with members:
    - "residual guide feature fusion network for single image deraining"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "density aware single image de raining using a multi stream dense network"
    - "renoir a dataset for real low light image noise reduction"
    - "vital visual tracking via adversarial learning"
    - "what can help pedestrian detection"
    - "direction aware spatial context features for shadow detection"

Connected through hyperedge HE-3 (induced by source paper "heavy rain image restoration integrating physics model and conditional adversarial learning"):
  with members:
    - "fast end to end trainable guided filter"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "playing for benchmarks"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"
    - "dehazenet an end to end system for single image haze removal"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-4 (induced by source paper "fastderain a novel video rain streak removal method using directional gradient priors"):
  with members:
    - "learning dual convolutional neural networks for low level vision"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "density aware single image de raining using a multi stream dense network"
    - "desnownet context aware deep network for snow removal"
    - "a novel nonconvex approach to recover the low tubal rank tensor data when t svd meets pssv"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "benchmarking single image dehazing and beyond"
    - "attentive generative adversarial network for raindrop removal from a single image"

Connected through hyperedge HE-5 (induced by source paper "detecting driveable area for autonomous vehicles"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "mask r cnn"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "going deeper with convolutions"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-6 (induced by source paper "removing rain in videos a large scale database and a two stream convlstm approach"):
  with members:
    - "residual guide feature fusion network for single image deraining"
    - "density aware single image de raining using a multi stream dense network"
    - "densely connected convolutional networks"
    - "deep joint rain detection and removal from a single image"

Connected through hyperedge HE-7 (induced by source paper "robust video content alignment and compensation for clear vision through the rain"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "robust online matrix factorization for dynamic background subtraction"
    - "deep joint rain detection and removal from a single image"
    - "accurate image super resolution using very deep convolutional networks"
    - "adam a method for stochastic optimization"
    - "matconvnet convolutional neural networks for matlab"
    - "robust principal component analysis"

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)