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: "a d net training a shadow detector with adversarial shadow attenuation"
  Abstract: We propose a novel GAN-based framework for detecting shadows in images, in which a shadow detection network (D-Net) is trained together with a shadow attenuation network (A-Net) that generates adversarial training examples. The A-Net modifies the original training images constrained by a simplified physical shadow model and is focused on fooling the D-Net's shadow predictions. Hence, it is effectively augmenting the training data for D-Net with hard-to-predict cases. The D-Net is trained to predict shadows in both original images and generated images from the A-Net. Our experimental results show that the additional training data from A-Net significantly improves the shadow detection accuracy of D-Net. Our method outperforms the state-of-the-art methods on the most challenging shadow detection benchmark (SBU) and also obtains state-of-the-art results on a cross-dataset task, testing on UCF. Furthermore, the proposed method achieves accurate real-time shadow detection at 45 frames per second.

Connected through hyperedge HE-1 (induced by source paper "where is my mirror"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "salient objects in clutter bringing salient object detection to the foreground"
    - "path aggregation network for instance segmentation"
    - "reverse attention for salient object detection"
    - "cbam convolutional block attention module"
    - "context encoding for semantic segmentation"
    - "direction aware spatial context features for shadow detection"
    - "picanet learning pixel wise contextual attention for 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"
    - "direction aware spatial context features for shadow detection and removal"
    - "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"
    - "direction aware spatial context features for shadow detection and removal"

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"
    - "direction aware spatial context features for shadow detection and removal"
    - "drit diverse image to image translation via disentangled representations"
    - "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 "shadow removal via shadow image decomposition"):
  with members:
    - "weakly labeling the antarctic the penguin colony case"
    - "progressive growing of gans for improved quality stability and variation"
    - "co localization with category consistent features and geodesic distance propagation"
    - "stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "interactive removal and ground truth for difficult shadow scenes"
    - "aggregated residual transformations for deep neural networks"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-6 (induced by source paper "weakly labeling the antarctic the penguin colony case"):
  with members:
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "deep extreme cut from extreme points to object segmentation"
    - "co localization with category consistent features and geodesic distance propagation"
    - "aggregated residual transformations for deep neural networks"
    - "learning from simulated and unsupervised images through adversarial training"
    - "geodesic distance histogram feature for video segmentation"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "generate segment and replace towards generic manipulation segmentation"):
  with members:
    - "learning rich features for image manipulation detection"
    - "image provenance analysis at scale"
    - "fighting fake news image splice detection via learned self consistency"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "image splicing localization using a multi task fully convolutional network mfcn"
    - "two stream neural networks for tampered face detection"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "progressive growing of gans for improved quality stability and variation"

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)