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 "automatic real time background cut for portrait videos"):
  Abstract: We in this paper solve the problem of high-quality automatic real-time background cut for 720p portrait videos. We first handle the background ambiguity issue in semantic segmentation by proposing a global background attenuation model. A spatial-temporal refinement network is developed to further refine the segmentation errors in each frame and ensure temporal coherence in the segmentation map. We form an end-to-end network for training and testing. Each module is designed considering efficiency and accuracy. We build a portrait dataset, which includes 8,000 images with high-quality labeled map for training and testing. To further improve the performance, we build a portrait video dataset with 50 sequences to fine-tune video segmentation. Our framework benefits many video processing applications.

Includes members:
  Member "enet a deep neural network architecture for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "stfcn spatio temporal fcn for semantic video segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach")
  Member "semantic video segmentation by gated recurrent flow propagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semantic flow for fast and accurate scene parsing")
  Member "unsupervised video segmentation via spatio temporally nonlocal appearance learning":
    also connected through other hyperedges:
      (none)
  Member "recurrent fully convolutional networks for video segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the texture bias for few shot cnn segmentation")
  Member "clockwork convnets for video semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "semi supervised domain adaptation for weakly labeled semantic video object segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "video object segmentation and tracking a survey")

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)