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 "realizing pixel level semantic learning in complex driving scenes based on only one annotated pixel per class"):
  Abstract: Semantic segmentation tasks based on weakly supervised condition have been put forward to achieve a lightweight labeling process. For simple images that only include a few categories, researches based on image-level annotations have achieved acceptable performance. However, when facing complex scenes, since image contains a large amount of classes, it becomes difficult to learn visual appearance based on image tags. In this case, image-level annotations are not effective in providing information. Therefore, we set up a new task in which only one annotated pixel is provided for each category. Based on the more lightweight and informative condition, a three step process is built for pseudo labels generation, which progressively implement optimal feature representation for each category, image inference and context-location based refinement. In particular, since high-level semantics and low-level imaging feature have different discriminative ability for each class under driving scenes, we divide each category into "object" or "scene" and then provide different operations for the two types separately. Further, an alternate iterative structure is established to gradually improve segmentation performance, which combines CNN-based inter-image common semantic learning and imaging prior based intra-image modification process. Experiments on Cityscapes dataset demonstrate that the proposed method provides a feasible way to solve weakly supervised semantic segmentation task under complex driving scenes.

Includes members:
  Member "confidence regularized self training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross domain self supervised learning for domain adaptation with few source labels")
  Member "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mask based unsupervised content transfer")
  Member "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "weakly supervised semantic segmentation by iteratively mining common object features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "weakly and semi supervised panoptic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards bounding box free panoptic segmentation")
  Member "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning video object segmentation from unlabeled videos")

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)