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 "mask based unsupervised content transfer"):
  Abstract: We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other. The proposed method disentangles the common and separate parts of these domains and, through the generation of a mask, focuses the attention of the underlying network to the desired augmentation alone, without wastefully reconstructing the entire target. This enables state-of-the-art quality and variety of content translation, as shown through extensive quantitative and qualitative evaluation. Furthermore, the novel mask-based formulation and regularization is accurate enough to achieve state-of-the-art performance in the realm of weakly supervised segmentation, where only class labels are given. To our knowledge, this is the first report that bridges the problems of domain disentanglement and weakly supervised segmentation. Our code is publicly available at this https URL.

Includes members:
  Member "emerging disentanglement in auto encoder based unsupervised image content transfer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nestedvae isolating common factors via weak supervision")
  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 "realizing pixel level semantic learning in complex driving scenes based on only one annotated pixel per class")
  Member "stgan a unified selective transfer network for arbitrary image attribute editing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "towards content transfer through grounded text generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "select and attend towards controllable content selection in text generation")
  Member "audio style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "applying visual domain style transfer and texture synthesis techniques to audio insights and challenges")
  Member "weakly supervised instance segmentation using class peak response":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "self produced guidance for weakly supervised object localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "starnet towards weakly supervised few shot detection and explainable few shot classification")
  Member "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)