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: "direction aware spatial context features for shadow detection and removal"
  Abstract: Shadow detection and shadow removal are fundamental and challenging tasks, requiring an understanding of the global image semantics. This paper presents a novel deep neural network design for shadow detection and removal by analyzing the image context in a direction-aware manner. To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN. By learning these weights through training, we can recover direction-aware spatial context (DSC) for detecting and removing shadows. This design is developed into the DSC module and embedded in a convolutional neural network (CNN) to learn the DSC features in different levels. Moreover, we design a weighted cross entropy loss to make effective the training for shadow detection and further adopt the network for shadow removal by using a Euclidean loss function and formulating a color transfer function to address the color and luminosity inconsistency in the training pairs. We employ two shadow detection benchmark datasets and two shadow removal benchmark datasets, and perform various experiments to evaluate our method. Experimental results show that our method clearly outperforms state-of-the-art methods for both shadow detection and shadow removal.

Connected through hyperedge HE-1 (induced by source paper "sac net spatial attenuation context for salient object detection"):
  with members:
    - "salient object detection with lossless feature reflection and weighted structural loss"
    - "pyramid feature attention network for saliency detection"
    - "a simple pooling based design for real time salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "group normalization"
    - "reverse attention for salient object detection"
    - "learning uncertain convolutional features for accurate saliency detection"

Connected through hyperedge HE-2 (induced by source paper "argan attentive recurrent generative adversarial network for shadow detection and removal"):
  with members:
    - "learning intrinsic image decomposition from watching the world"
    - "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"
    - "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-3 (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"
    - "a d net training a shadow detector with adversarial shadow attenuation"

Connected through hyperedge HE-4 (induced by source paper "mask shadowgan learning to remove shadows from unpaired data"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "drit diverse image to image translation via disentangled representations"
    - "a d net training a shadow detector with adversarial shadow attenuation"
    - "transitive invariance for self supervised visual representation learning"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "direction aware spatial context features for shadow detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal"

Connected through hyperedge HE-5 (induced by source paper "instance shadow detection"):
  with members:
    - "tensormask a foundation for dense object segmentation"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "megdet a large mini batch object detector"
    - "stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "mask r cnn"
    - "boundary aware instance segmentation"
    - "deep watershed transform for instance segmentation"

Connected through hyperedge HE-6 (induced by source paper "revisiting shadow detection a new benchmark dataset for complex world"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "where is my mirror"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal"
    - "xception deep learning with depthwise separable convolutions"
    - "pyramid scene parsing network"
    - "parsenet looking wider to see better"

Connected through hyperedge HE-7 (induced by source paper "conditional gans for multi illuminant color constancy revolution or yet another approach"):
  with members:
    - "video to video synthesis"
    - "multimodal unsupervised image to image translation"
    - "artificial color constancy via googlenet with angular loss function"
    - "progressive growing of gans for improved quality stability and variation"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "fast shadow detection from a single image using a patched convolutional neural network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial 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)