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 "video propagation networks"):
  Abstract: We propose a technique that propagates information forward through video data. The method is conceptually simple and can be applied to tasks that require the propagation of structured information, such as semantic labels, based on video content. We propose a 'Video Propagation Network' that processes video frames in an adaptive manner. The model is applied online: it propagates information forward without the need to access future frames. In particular we combine two components, a temporal bilateral network for dense and video adaptive filtering, followed by a spatial network to refine features and increased flexibility. We present experiments on video object segmentation and semantic video segmentation and show increased performance comparing to the best previous task-specific methods, while having favorable runtime. Additionally we demonstrate our approach on an example regression task of color propagation in a grayscale video.

Includes members:
  Member "fast optical flow using dense inverse search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning the sense of touch in simulation a sim to real strategy for vision based tactile sensing")
  Member "learning video object segmentation from static images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "clockwork convnets for video semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient semantic video segmentation with per frame inference")
  Member "one shot video object segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "playing for data ground truth from computer games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "learning sparse high dimensional filters image filtering dense crfs and bilateral neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "building footprint generation by integratingconvolution neural network with feature pairwiseconditional random field fpcrf")
  Member "conditional random fields as recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")
  Member "semantic video segmentation exploring inference efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "face mask extraction in video sequence")

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)