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 "high quality automatic foreground extraction using consensus equilibrium"):
  Abstract: Extracting accurate foreground objects from a scene is a fundamental step in creating virtual reality content. However, majority of the professional softwares today still require human interventions, e.g., providing trimaps or labeling key frames. This is not only time consuming, but is also error prone. In this paper, we present a fully automatic algorithm to extract foreground objects. Our solution is based on a newly developed concept called the Multi-Agent Consensus Equilibrium (MACE), a framework which allows us to integrate multiple sources of expertise to produce an overall superior result. Our MACE framework consists of three agents: (1) A new dual layer closed-form matting agent to estimate the foreground mask using the color image and a background image; (2) A background probability estimator using color difference and object segmentation; (3) A total variation minimization agent to control the smoothness of the foreground masks. We show how these agents are constructed, and how their interactions lead to better performance. The algorithm is evaluated by comparing to several state-of-the-art methods. Extensive experimental study shows that the proposed method has less error compared to the competing methods.

Includes members:
  Member "deep image matting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "learning deep cnn denoiser prior for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse weight activation training")
  Member "plug and play priors for bright field electron tomography and sparse interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "plug and play methods provably converge with properly trained denoisers")

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)