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: "iterative instance segmentation"
  Abstract: Existing methods for pixel-wise labelling tasks generally disregard the underlying structure of labellings, often leading to predictions that are visually implausible. While incorporating structure into the model should improve prediction quality, doing so is challenging - manually specifying the form of structural constraints may be impractical and inference often becomes intractable even if structural constraints are given. We sidestep this problem by reducing structured prediction to a sequence of unconstrained prediction problems and demonstrate that this approach is capable of automatically discovering priors on shape, contiguity of region predictions and smoothness of region contours from data without any a priori specification. On the instance segmentation task, this method outperforms the state-of-the-art, achieving a mean $\mathrm{AP}^{r}$ of 63.6% at 50% overlap and 43.3% at 70% overlap.

Connected through hyperedge HE-1 (induced by source paper "object contextual representations for semantic segmentation"):
  with members:
    - "learning compositional neural information fusion for human parsing"
    - "acfnet attentional class feature network for semantic segmentation"
    - "dual graph convolutional network for semantic segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "global aggregation then local distribution in fully convolutional networks"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"

Connected through hyperedge HE-2 (induced by source paper "distributed iterative gating networks for semantic segmentation"):
  with members:
    - "gated multiple feedback network for image super resolution"
    - "recurrent iterative gating networks for semantic segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "recurrent scene parsing with perspective understanding in the loop"
    - "convolutional networks with adaptive inference graphs"
    - "iterative deep convolutional encoder decoder network for medical image segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"

Connected through hyperedge HE-3 (induced by source paper "deep back projection networks for single image super resolution"):
  with members:
    - "frame recurrent video super resolution"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "sketch based manga retrieval using manga109 dataset"
    - "enhanced deep residual networks for single image super resolution"
    - "detail revealing deep video super resolution"

Connected through hyperedge HE-4 (induced by source paper "etnet error transition network for arbitrary style transfer"):
  with members:
    - "attention aware multi stroke style transfer"
    - "a style aware content loss for real time hd style transfer"
    - "learning linear transformations for fast arbitrary style transfer"
    - "difnet semantic segmentation by diffusion networks"
    - "style separation and synthesis via generative adversarial networks"
    - "deep back projection networks for super resolution"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "nonlocal neural networks nonlocal diffusion and nonlocal modeling"

Connected through hyperedge HE-5 (induced by source paper "iterative fully convolutional neural networks for automatic vertebra segmentation and identification"):
  with members:
    - "joint vertebrae identification and localization in spinal ct images by combining short and long range contextual information"
    - "semi convolutional operators for instance segmentation"
    - "a survey on deep learning in medical image analysis"
    - "fully automatic segmentation of lumbar vertebrae from ct images using cascaded 3d fully convolutional networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "automatic vertebra labeling in large scale 3d ct using deep image to image network with message passing and sparsity regularization"
    - "a localisation segmentation approach for multi label annotation of lumbar vertebrae using deep nets"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "deepilluminance contextual illuminance estimation via deep neural networks"):
  with members:
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "joint material and illumination estimation from photo sets in the wild"
    - "stacked hourglass networks for human pose estimation"
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "single and multiple illuminant estimation using convolutional neural networks"
    - "learning to compare image patches via convolutional neural networks"
    - "color constancy using cnns"

Connected through hyperedge HE-7 (induced by source paper "towards locally consistent object counting with constrained multi stage convolutional neural networks"):
  with members:
    - "switching convolutional neural network for crowd counting"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"
    - "understanding traffic density from large scale web camera data"
    - "a survey of recent advances in cnn based single image crowd counting and density estimation"
    - "stacked hourglass networks for human pose estimation"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-8 (induced by source paper "efficient coarse to fine non local module for the detection of small objects"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "ocnet object context network for scene parsing"
    - "yolov3 an incremental improvement"
    - "compact generalized non local network"
    - "auto context r cnn"
    - "relational inductive biases deep learning and graph networks"
    - "non local recurrent network for image restoration"
    - "couplenet coupling global structure with local parts for object detection"

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)