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 "weakly supervised instance segmentation by deep community learning"):
  Abstract: We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks. This task is formulated as a combination of weakly supervised object detection and semantic segmentation, where individual objects of the same class are identified and segmented separately. We address this problem by designing a unified deep neural network architecture, which has a positive feedback loop of object detection with bounding box regression, instance mask generation, instance segmentation, and feature extraction. Each component of the network makes active interactions with others to improve accuracy, and the end-to-end trainability of our model makes our results more robust and reproducible. The proposed algorithm achieves state-of-the-art performance in the weakly supervised setting without any additional training such as Fast R-CNN and Mask R-CNN on the standard benchmark dataset.

Includes members:
  Member "weakly supervised learning of instance segmentation with inter pixel relations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep affinity net instance segmentation via affinity")
  Member "weakly supervised object detection with segmentation collaboration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ehsod cam guided end to end hybrid supervised object detection with cascade refinement")
  Member "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "min entropy latent model for weakly supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ehsod cam guided end to end hybrid supervised object detection with cascade refinement")
  Member "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "realizing pixel level semantic learning in complex driving scenes based on only one annotated pixel per class")
  Member "where are the masks instance segmentation with image level supervision":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards partial supervision for generic object counting in natural scenes")
  Member "object counting and instance segmentation with image level supervision":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "psc net learning part spatial co occurence for occluded pedestrian detection")
  Member "weakly supervised instance segmentation using class peak response":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "explainable deep classification models for domain generalization")

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)