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: "learning random walk label propagation for weakly supervised semantic segmentation"
  Abstract: Large-scale training for semantic segmentation is challenging due to the expense of obtaining training data for this task relative to other vision tasks. We propose a novel training approach to address this difficulty. Given cheaply-obtained sparse image labelings, we propagate the sparse labels to produce guessed dense labelings. A standard CNN-based segmentation network is trained to mimic these labelings. The label-propagation process is defined via random-walk hitting probabilities, which leads to a differentiable parameterization with uncertainty estimates that are incorporated into our loss. We show that by learning the label-propagator jointly with the segmentation predictor, we are able to effectively learn semantic edges given no direct edge supervision. Experiments also show that training a segmentation network in this way outperforms the naive approach.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised salient object detection via scribble annotations"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "gated crf loss for weakly supervised semantic image segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "egnet edge guidance network for salient object detection"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "weakly supervised salient object detection using image labels"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "gated crf loss for weakly supervised semantic image segmentation"):
  with members:
    - "rethinking imagenet pre training"
    - "towards closing the gap in weakly supervised semantic segmentation with dcnns combining local and global models"
    - "convolutional crfs for semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "efficient relaxations for dense crfs with sparse higher order potentials"
    - "on regularized losses for weakly supervised cnn segmentation"

Connected through hyperedge HE-3 (induced by source paper "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"):
  with members:
    - "learning to exploit the prior network knowledge for weakly supervised semantic segmentation"
    - "hybrid task cascade for instance segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "weakly and semi supervised panoptic segmentation"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised learning of instance segmentation with inter pixel relations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "weakly supervised instance segmentation using class peak response"
    - "learning to segment via cut and paste"
    - "group normalization"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-5 (induced by source paper "joint learning of saliency detection and weakly supervised semantic segmentation"):
  with members:
    - "multi source weak supervision for saliency detection"
    - "semantic correlation promoted shape variant context for segmentation"
    - "boundary aware feature propagation for scene segmentation"
    - "salient objects in clutter bringing salient object detection to the foreground"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "reverse attention for salient object detection"
    - "learning uncertain convolutional features for accurate saliency detection"

Connected through hyperedge HE-6 (induced by source paper "reliability does matter an end to end weakly supervised semantic segmentation approach"):
  with members:
    - "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "deeply supervised salient object detection with short connections"
    - "self produced guidance for weakly supervised object localization"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "adversarial complementary learning for weakly supervised object localization"
    - "efficient relaxations for dense crfs with sparse higher order potentials"

Connected through hyperedge HE-7 (induced by source paper "constrained cnn losses for weakly supervised segmentation"):
  with members:
    - "constrained deep learning using conditional gradient and applications in computer vision"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "imposing hard constraints on deep networks promises and limitations"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "smooth adversarial examples"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial attacks and defences competition"
    - "harmonic adversarial attack method"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "a closed form solution to photorealistic image stylization"
    - "a flexible convolutional solver with application to photorealistic style transfer"
    - "low frequency adversarial perturbation"
    - "deep photo style transfer"

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)