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: "discovering class specific pixels for weakly supervised semantic segmentation"
  Abstract: We propose an approach to discover class-specific pixels for the weakly-supervised semantic segmentation task. We show that properly combining saliency and attention maps allows us to obtain reliable cues capable of significantly boosting the performance. First, we propose a simple yet powerful hierarchical approach to discover the class-agnostic salient regions, obtained using a salient object detector, which otherwise would be ignored. Second, we use fully convolutional attention maps to reliably localize the class-specific regions in a given image. We combine these two cues to discover class-specific pixels which are then used as an approximate ground truth for training a CNN. While solving the weakly supervised semantic segmentation task, we ensure that the image-level classification task is also solved in order to enforce the CNN to assign at least one pixel to each object present in the image. Experimentally, on the PASCAL VOC12 val and test sets, we obtain the mIoU of 60.8% and 61.9%, achieving the performance gains of 5.1% and 5.2% compared to the published state-of-the-art results. The code is made publicly available.

Connected through hyperedge HE-1 (induced by source paper "ha ccn hierarchical attention based crowd counting network"):
  with members:
    - "inverse attention guided deep crowd counting network"
    - "crowd counting and density estimation by trellis encoder decoder network"
    - "few example object detection with model communication"
    - "mvx net multimodal voxelnet for 3d object detection"
    - "dafe fd density aware feature enrichment for face detection"
    - "learning from synthetic data for crowd counting in the wild"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"

Connected through hyperedge HE-2 (induced by source paper "what synthesis is missing depth adaptation integrated with weak supervision for indoor scene parsing"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "weakly and semi supervised panoptic segmentation"
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "harvesting information from captions for weakly supervised semantic segmentation"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "on regularized losses for weakly supervised cnn segmentation"

Connected through hyperedge HE-4 (induced by source paper "discovering latent classes for semi supervised semantic segmentation"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"
    - "curriculum based dropout discriminator for domain adaptation"
    - "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation"
    - "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"
    - "constructing self motivated pyramid curriculums for cross domain semantic segmentation a non adversarial approach"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "saliency guided self attention network for weakly and semi supervised semantic segmentation"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "self supervised difference detection 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 supervised scale equivariant network for weakly supervised semantic segmentation"
    - "coarse to fine semantic segmentation from image level labels"
    - "ocnet object context network for scene parsing"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation"):
  with members:
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "deeply supervised salient object detection with short connections"
    - "learning a discriminative feature network for semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "adversarial complementary learning for weakly supervised object localization"
    - "robust tumor localization with pyramid grad cam"

Connected through hyperedge HE-7 (induced by source paper "self supervised difference detection for weakly supervised semantic segmentation"):
  with members:
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "tell me where to look guided attention inference network"
    - "self erasing network for integral object attention"
    - "weakly supervised semantic segmentation using web crawled videos"

Connected through hyperedge HE-8 (induced by source paper "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "adversarial complementary learning for weakly supervised object localization"
    - "robust tumor localization with pyramid grad cam"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

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)