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: "the devil is in the decoder classification regression and gans"
  Abstract: Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image. Models for such problems usually consist of encoders which decrease spatial resolution while learning a high-dimensional representation, followed by decoders who recover the original input resolution and result in low-dimensional predictions. While encoders have been studied rigorously, relatively few studies address the decoder side. This paper presents an extensive comparison of a variety of decoders for a variety of pixel-wise tasks ranging from classification, regression to synthesis. Our contributions are: (1) Decoders matter: we observe significant variance in results between different types of decoders on various problems. (2) We introduce new residual-like connections for decoders. (3) We introduce a novel decoder: bilinear additive upsampling. (4) We explore prediction artifacts.

Connected through hyperedge HE-1 (induced by source paper "neural architecture search for deep image prior"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "autogan neural architecture search for generative adversarial networks"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "progressive neural architecture search"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-2 (induced by source paper "spgnet semantic prediction guidance for scene parsing"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "seamless scene segmentation"
    - "deeperlab single shot image parser"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "imagined value gradients model based policy optimization with transferable latent dynamics models"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "model based reinforcement learning for atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "information asymmetry in kl regularized rl"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "combined reinforcement learning via abstract representations"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-4 (induced by source paper "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "context encoding for semantic segmentation"
    - "stacked deconvolutional network for semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "bridging category level and instance level semantic image segmentation"

Connected through hyperedge HE-5 (induced by source paper "depthwisegans fast training generative adversarial networks for realistic image synthesis"):
  with members:
    - "generative adversarial networks using adaptive convolution"
    - "stabilizing gan training with multiple random projections"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "mmgan manifold matching generative adversarial network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-6 (induced by source paper "augmentation for small object detection"):
  with members:
    - "feature fused ssd fast detection for small objects"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "dssd deconvolutional single shot detector"
    - "perceptual generative adversarial networks for small object detection"
    - "improving small object proposals for company logo detection"
    - "semantic instance segmentation via deep metric learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "mask r cnn"

Connected through hyperedge HE-7 (induced by source paper "attacking optical flow"):
  with members:
    - "learning human optical flow"
    - "robust physical world attacks on deep learning models"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "back to basics unsupervised learning of optical flow via brightness constancy and motion smoothness"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-8 (induced by source paper "a network structure to explicitly reduce confusion errors in semantic segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "training region based object detectors with online hard example mining"

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)