human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "saliency guided self attention network for weakly and semi supervised semantic segmentation"):
  Abstract: Weakly supervised semantic segmentation (WSSS) using only image-level labels can greatly reduce the annotation cost and therefore has attracted considerable research interest. However, its performance is still inferior to the fully supervised counterparts. To mitigate the performance gap, we propose a saliency guided self-attention network (SGAN) to address the WSSS problem. The introduced self-attention mechanism is able to capture rich and extensive contextual information but may mis-spread attentions to unexpected regions. In order to enable this mechanism to work effectively under weak supervision, we integrate class-agnostic saliency priors into the self-attention mechanism and utilize class-specific attention cues as an additional supervision for SGAN. Our SGAN is able to produce dense and accurate localization cues so that the segmentation performance is boosted. Moreover, by simply replacing the additional supervisions with partially labeled ground-truth, SGAN works effectively for semi-supervised semantic segmentation as well. Experiments on the PASCAL VOC 2012 and COCO datasets show that our approach outperforms all other state-of-the-art methods in both weakly and semi-supervised settings.

Includes members:
  Member "interlaced sparse self attention for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images")
  Member "self supervised difference detection for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      (none)
  Member "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "deeply supervised salient object detection with short connections":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "self supervised scale equivariant network for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      (none)
  Member "coarse to fine semantic segmentation from image level labels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a multi task u net for segmentation with lazy labels")
  Member "ocnet object context network for scene parsing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")

Question: Please tell me which class the source paper 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)