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: "shadow optimization from structured deep edge detection"
  Abstract: Local structures of shadow boundaries as well as complex interactions of image regions remain largely unexploited by previous shadow detection approaches. In this paper, we present a novel learning-based framework for shadow region recovery from a single image. We exploit the local structures of shadow edges by using a structured CNN learning framework. We show that using the structured label information in the classification can improve the local consistency of the results and avoid spurious labelling. We further propose and formulate a shadow/bright measure to model the complex interactions among image regions. The shadow and bright measures of each patch are computed from the shadow edges detected in the image. Using the global interaction constraints on patches, we formulate a least-square optimization problem for shadow recovery that can be solved efficiently. Our shadow recovery method achieves state-of-the-art results on the major shadow benchmark databases collected under various conditions.

Connected through hyperedge HE-1 (induced by source paper "towards ghost free shadow removal via dual hierarchical aggregation network and shadow matting gan"):
  with members:
    - "mask shadowgan learning to remove shadows from unpaired data"
    - "shadow removal via shadow image decomposition"
    - "argan attentive recurrent generative adversarial network for shadow detection and removal"
    - "free form image inpainting with gated convolution"
    - "single image reflection separation with perceptual losses"
    - "conditional gans for multi illuminant color constancy revolution or yet another approach"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "direction aware spatial context features for shadow detection and removal"

Connected through hyperedge HE-2 (induced by source paper "direction aware spatial context features for shadow detection and removal"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "intrinsic image transformation via scale space decomposition"
    - "fast shadow detection from a single image using a patched convolutional neural network"
    - "direction aware spatial context features for shadow detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal"
    - "training region based object detectors with online hard example mining"
    - "pyramid scene parsing network"

Connected through hyperedge HE-3 (induced by source paper "direction aware spatial context features for shadow detection"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "fast shadow detection from a single image using a patched convolutional neural network"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "pyramid scene parsing network"
    - "holistically nested edge detection"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-4 (induced by source paper "deep structured learning for facial action unit intensity estimation"):
  with members:
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "learning deep structured models"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

Connected through hyperedge HE-5 (induced by source paper "fast shadow detection from a single image using a patched convolutional neural network"):
  with members:
    - "corola a sequential solution to moving object detection using low rank approximation"

Connected through hyperedge HE-6 (induced by source paper "makeup like a superstar deep localized makeup transfer network"):
  with members:
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "a neural algorithm of artistic style"
    - "deep convolutional inverse graphics network"
    - "matching cnn meets knn quasi parametric human parsing"
    - "deep human parsing with active template regression"
    - "texture synthesis using convolutional neural networks"
    - "convolutional feature masking for joint object and stuff segmentation"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-7 (induced by source paper "fractal dimension invariant filtering and its cnn based implementation"):
  with members:
    - "object contour detection with a fully convolutional encoder decoder network"
    - "holistically nested edge detection"
    - "a neural algorithm of artistic style"
    - "deep residual learning for image recognition"
    - "deepedge a multi scale bifurcated deep network for top down contour detection"
    - "deep roto translation scattering for object classification"
    - "deformable part models are convolutional neural networks"
    - "fast edge detection using structured forests"

Connected through hyperedge HE-8 (induced by source paper "a fractal based cnn for detecting complicated curves in afm images"):
  with members:
    - "holistically nested edge detection"
    - "deepedge a multi scale bifurcated deep network for top down contour detection"
    - "deformable part models are convolutional neural networks"
    - "fast edge detection using structured forests"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "invariant scattering convolution networks"

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)