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: "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
  Abstract: Despite the remarkable progress, weakly supervised segmentation approaches are still inferior to their fully supervised counterparts. We obverse the performance gap mainly comes from their limitation on learning to produce high-quality dense object localization maps from image-level supervision. To mitigate such a gap, we revisit the dilated convolution [1] and reveal how it can be utilized in a novel way to effectively overcome this critical limitation of weakly supervised segmentation approaches. Specifically, we find that varying dilation rates can effectively enlarge the receptive fields of convolutional kernels and more importantly transfer the surrounding discriminative information to non-discriminative object regions, promoting the emergence of these regions in the object localization maps. Then, we design a generic classification network equipped with convolutional blocks of different dilated rates. It can produce dense and reliable object localization maps and effectively benefit both weakly- and semi- supervised semantic segmentation. Despite the apparent simplicity, our proposed approach obtains superior performance over state-of-the-arts. In particular, it achieves 60.8% and 67.6% mIoU scores on Pascal VOC 2012 test set in weakly- (only image-level labels are available) and semi- (1,464 segmentation masks are available) supervised settings, which are the new state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization"):
  with members:
    - "deep neural networks improve radiologists performance in breast cancer screening"
    - "processing megapixel images with deep attention sampling models"
    - "pseudoedgenet nuclei segmentation only with point annotations"
    - "constrained cnn losses for weakly supervised segmentation"
    - "learning from suspected target bootstrapping performance for breast cancer detection in mammography"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in x ray images"
    - "mammo a deep learning solution for facilitating radiologist machine collaboration in breast cancer diagnosis"

Connected through hyperedge HE-2 (induced by source paper "dense dilated convolutions merging network for land cover classification"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "dense fusion classmate network for land cover classification"
    - "on the convergence of adam and beyond"
    - "dense dilated convolutions merging network for semantic mapping of remote sensing images"
    - "sdc stacked dilated convolution a unified descriptor network for dense matching tasks"
    - "uncertainty gated network for land cover segmentation"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "urban land cover classification with missing data modalities using deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "differential treatment for stuff and things a simple unsupervised domain adaptation method for semantic segmentation"):
  with members:
    - "alignseg feature aligned segmentation networks"
    - "domain adaptation for semantic segmentation with maximum squares loss"
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "constructing self motivated pyramid curriculums for cross domain semantic segmentation a non adversarial approach"
    - "panoptic deeplab a simple strong and fast baseline for bottom up panoptic segmentation"
    - "transfer adaptation learning a decade survey"

Connected through hyperedge HE-4 (induced by source paper "rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation"):
  with members:
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "unsupervised domain adaptation via calibrating uncertainties"
    - "dual graph convolutional network for semantic segmentation"
    - "unsupervised scene adaptation with memory regularization in vivo"
    - "confidence regularized self training"
    - "an adversarial perturbation oriented domain adaptation approach for semantic segmentation"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "domain adaptation for structured output via discriminative patch representations"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised semantic segmentation by iterative affinity learning"):
  with members:
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "edge preserving and multi scale contextual neural network for salient object detection"
    - "learning affinity via spatial propagation networks"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "seed expand and constrain three principles for weakly supervised image segmentation"

Connected through hyperedge HE-6 (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"
    - "enhanced alignment measure for binary foreground map evaluation"

Connected through hyperedge HE-7 (induced by source paper "foal fast online adaptive learning for cardiac motion estimation"):
  with members:
    - "bridging stereo matching and optical flow via spatiotemporal correspondence"
    - "learning a probabilistic model for diffeomorphic registration"
    - "deep compressed sensing"
    - "direct object recognition without line of sight using optical coherence"
    - "iterative residual refinement for joint optical flow and occlusion estimation"
    - "selflow self supervised learning of optical flow"
    - "learning to self train for semi supervised few shot classification"
    - "unsupervised event based learning of optical flow depth and egomotion"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised segmentation of cracks on solar cells using normalized lp norm"):
  with members:
    - "automatic classification of defective photovoltaic module cells in electroluminescence images"
    - "segmentation of photovoltaic module cells in electroluminescence images"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "deep residual learning for image recognition"
    - "learned norm pooling for deep feedforward and recurrent neural 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)