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: "decoupled spatial neural attention for weakly supervised semantic segmentation"
  Abstract: Weakly supervised semantic segmentation receives much research attention since it alleviates the need to obtain a large amount of dense pixel-wise ground-truth annotations for the training images. Compared with other forms of weak supervision, image labels are quite efficient to obtain. In our work, we focus on the weakly supervised semantic segmentation with image label annotations. Recent progress for this task has been largely dependent on the quality of generated pseudo-annotations. In this work, inspired by spatial neural-attention for image captioning, we propose a decoupled spatial neural attention network for generating pseudo-annotations. Our decoupled attention structure could simultaneously identify the object regions and localize the discriminative parts which generates high-quality pseudo-annotations in one forward path. The generated pseudo-annotations lead to the segmentation results which achieve the state-of-the-art in weakly-supervised semantic segmentation.

Connected through hyperedge HE-1 (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-2 (induced by source paper "leveraging pretrained image classifiers for language based segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to exploit the prior network knowledge for weakly supervised semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "rtseg real time semantic segmentation comparative study"
    - "taskonomy disentangling task transfer learning"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "object level representation learning for few shot image classification"
    - "real time joint semantic segmentation and depth estimation using asymmetric annotations"

Connected through hyperedge HE-3 (induced by source paper "budget aware semi supervised semantic and instance segmentation"):
  with members:
    - "weakly supervised instance segmentation using class peak response"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "learning to segment via cut and paste"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "pseudo mask augmented object detection"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "coarse to fine semantic segmentation from image level labels"):
  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"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "unsupervised learning from video to detect foreground objects in single images"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "weakly supervised semantic segmentation based on web image co segmentation"

Connected through hyperedge HE-5 (induced by source paper "convolutional simplex projection network cspn for weakly supervised semantic segmentation"):
  with members:
    - "two phase learning for weakly supervised object localization"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "built in foreground background prior for weakly supervised semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "constrained convolutional neural networks for weakly supervised segmentation"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional multi class multiple instance learning"
    - "from image level to pixel level labeling with convolutional 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)