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 "learning structure via consensus for face segmentation and parsing"):
  Abstract: Face segmentation is the task of densely labeling pixels on the face according to their semantics. While current methods place an emphasis on developing sophisticated architectures, use conditional random fields for smoothness, or rather employ adversarial training, we follow an alternative path towards robust face segmentation and parsing. Occlusions, along with other parts of the face, have a proper structure that needs to be propagated in the model during training. Unlike state-of-the-art methods that treat face segmentation as an independent pixel prediction problem, we argue instead that it should hold highly correlated outputs within the same object pixels. We thereby offer a novel learning mechanism to enforce structure in the prediction via consensus, guided by a robust loss function that forces pixel objects to be consistent with each other. Our face parser is trained by transferring knowledge from another model, yet it encourages spatial consistency while fitting the labels. Different than current practice, our method enjoys pixel-wise predictions, yet paves the way for fewer artifacts, less sparse masks, and spatially coherent outputs.

Includes members:
  Member "fsgan subject agnostic face swapping and reenactment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the robustness of face recognition algorithms against attacks and bias")
  Member "does generative face completion help face recognition":
    also connected through other hyperedges:
      (none)
  Member "face mask extraction in video sequence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic face video segmentation via reinforcement learning")
  Member "learning discriminators as energy networks in adversarial learning":
    also connected through other hyperedges:
      (none)
  Member "adversarial structure matching loss for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "i bet you are wrong gambling adversarial networks for structured semantic segmentation")
  Member "adaptive affinity fields for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "high order paired aspp networks for semantic segmenation")
  Member "face completion with semantic knowledge and collaborative adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "domain embedded multi model generative adversarial networks for image based face inpainting")
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)