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: "weakly supervised object boundaries"
  Abstract: State-of-the-art learning based boundary detection methods require extensive training data. Since labelling object boundaries is one of the most expensive types of annotations, there is a need to relax the requirement to carefully annotate images to make both the training more affordable and to extend the amount of training data. In this paper we propose a technique to generate weakly supervised annotations and show that bounding box annotations alone suffice to reach high-quality object boundaries without using any object-specific boundary annotations. With the proposed weak supervision techniques we achieve the top performance on the object boundary detection task, outperforming by a large margin the current fully supervised state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "attention based dropout layer for weakly supervised object localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "few example object detection with model communication"
    - "bam bottleneck attention module"
    - "image transformer"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "adversarial complementary learning for weakly supervised object localization"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-2 (induced by source paper "deep crisp boundaries from boundaries to higher level tasks"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "understanding convolution for semantic segmentation"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "object contour detection with a fully convolutional encoder decoder network"

Connected through hyperedge HE-3 (induced by source paper "object contour and edge detection with refinecontournet"):
  with members:
    - "deep crisp boundaries from boundaries to higher level tasks"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "learning to predict crisp boundaries"
    - "boundary flow a siamese network that predicts boundary motion without training on motion"
    - "richer convolutional features for edge detection"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "slim densepose thrifty learning from sparse annotations and motion cues"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "densereg fully convolutional dense shape regression in the wild"
    - "unsupervised discovery of object landmarks as structural representations"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "densepose dense human pose estimation in the wild"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "mass displacement networks"
    - "end to end recovery of human shape and pose"

Connected through hyperedge HE-5 (induced by source paper "semantic edge detection with diverse deep supervision"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "deep crisp boundaries from boundaries to higher level tasks"
    - "dynamic feature fusion for semantic edge detection"
    - "deeply supervised salient object detection with short connections"
    - "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
    - "taskonomy disentangling task transfer learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "simultaneous edge alignment and learning"

Connected through hyperedge HE-6 (induced by source paper "w talc weakly supervised temporal activity localization and classification"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "temporal action detection with structured segment networks"
    - "deep self taught learning for weakly supervised object localization"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "transfer learning by ranking for weakly supervised object annotation"

Connected through hyperedge HE-7 (induced by source paper "simultaneous edge alignment and learning"):
  with members:
    - "boundary cues for 3d object shape recovery"
    - "iterative learning with open set noisy labels"
    - "casenet deep category aware semantic edge detection"
    - "richer convolutional features for edge detection"
    - "annotating object instances with a polygon rnn"
    - "toward robustness against label noise in training deep discriminative neural networks"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning from noisy labels with distillation"

Connected through hyperedge HE-8 (induced by source paper "the devil is in the decoder classification regression and gans"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "improved training of wasserstein gans"
    - "super resolution with deep adaptive image resampling"
    - "rethinking atrous convolution for semantic image segmentation"
    - "stacked hourglass networks for human pose estimation"
    - "flownet 2 0 evolution of optical flow estimation with deep 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)