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 "lafin generative landmark guided face inpainting"):
  Abstract: It is challenging to inpaint face images in the wild, due to the large variation of appearance, such as different poses, expressions and occlusions. A good inpainting algorithm should guarantee the realism of output, including the topological structure among eyes, nose and mouth, as well as the attribute consistency on pose, gender, ethnicity, expression, etc. This paper studies an effective deep learning based strategy to deal with these issues, which comprises of a facial landmark predicting subnet and an image inpainting subnet. Concretely, given partial observation, the landmark predictor aims to provide the structural information (e.g. topological relationship and expression) of incomplete faces, while the inpaintor is to generate plausible appearance (e.g. gender and ethnicity) conditioned on the predicted landmarks. Experiments on the CelebA-HQ and CelebA datasets are conducted to reveal the efficacy of our design and, to demonstrate its superiority over state-of-the-art alternatives both qualitatively and quantitatively. In addition, we assume that high-quality completed faces together with their landmarks can be utilized as augmented data to further improve the performance of (any) landmark predictor, which is corroborated by experimental results on the 300W and WFLW datasets.

Includes members:
  Member "few shot adversarial learning of realistic neural talking head models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "opengan open set generative adversarial networks")
  Member "sc fegan face editing generative adversarial network with user s sketch and color":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep plastic surgery robust and controllable image editing with human drawn sketches")
  Member "faceswapnet landmark guided many to many face reenactment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "marionette few shot face reenactment preserving identity of unseen targets")
  Member "foreground aware image inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fine grained image to image transformation towards visual recognition")
  Member "pluralistic image completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval")
  Member "edgeconnect generative image inpainting with adversarial edge learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "guidance and evaluation semantic aware image inpainting for mixed scenes")
  Member "look at boundary a boundary aware face alignment algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "kpnet towards minimal face detector")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)