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: "built in foreground background prior for weakly supervised semantic segmentation"
  Abstract: Pixel-level annotations are expensive and time consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recently, CNN-based methods have proposed to fine-tune pre-trained networks using image tags. Without additional information, this leads to poor localization accuracy. This problem, however, was alleviated by making use of objectness priors to generate foreground/background masks. Unfortunately these priors either require training pixel-level annotations/bounding boxes, or still yield inaccurate object boundaries. Here, we propose a novel method to extract markedly more accurate masks from the pre-trained network itself, forgoing external objectness modules. This is accomplished using the activations of the higher-level convolutional layers, smoothed by a dense CRF. We demonstrate that our method, based on these masks and a weakly-supervised loss, outperforms the state-of-the-art tag-based weakly-supervised semantic segmentation techniques. Furthermore, we introduce a new form of inexpensive weak supervision yielding an additional accuracy boost.

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 "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"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "bae net branched autoencoder for shape co segmentation"):
  with members:
    - "cosegnet deep co segmentation of 3d shapes with group consistency loss"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "deep part induction from articulated object pairs"
    - "3d point capsule networks"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "deep co training for semi supervised image segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "constrained cnn losses for weakly supervised segmentation"
    - "deeply supervised salient object detection with short connections"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "a two stream mutual attention network for semi supervised biomedical segmentation with noisy labels"
    - "deep co training for semi supervised image recognition"
    - "adversarial learning for semi supervised semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "unsupervised domain adaptation using generative adversarial networks for semantic segmentation of aerial images"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "road reality oriented adaptation for semantic segmentation of urban scenes"
    - "synthetic to real adaptation with generative correlation alignment networks"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-6 (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-7 (induced by source paper "label penet sequential label propagation and enhancement networks forweakly supervised instance segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "weakly supervised instance segmentation using class peak response"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "automated curriculum learning for neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "deep self taught learning for weakly supervised object localization"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-8 (induced by source paper "extremec3net extreme lightweight portrait segmentation networks using advanced c3 modules"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "character region awareness for text detection"
    - "extd extremely tiny face detector via iterative filter reuse"
    - "fast scnn fast semantic segmentation network"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "c3 concentrated comprehensive convolution and its application to semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"

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)