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 "face attribute invertion"):
  Abstract: Manipulating human facial images between two domains is an important and interesting problem. Most of the existing methods address this issue by applying two generators or one generator with extra conditional inputs. In this paper, we proposed a novel self-perception method based on GANs for automatical face attribute inverse. The proposed method takes face images as inputs and employs only one single generator without being conditioned on other inputs. Profiting from the multi-loss strategy and modified U-net structure, our model is quite stable in training and capable of preserving finer details of the original face images.

Includes members:
  Member "deep transfer across domains for face antispoofing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "domain agnostic feature learning for image and video based face anti spoofing")
  Member "enhance the motion cues for face anti spoofing using cnn lstm architecture":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "defending from adversarial examples with a two stream architecture")
  Member "learning generalizable and identity discriminative representations for face anti spoofing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep frequent spatial temporal learning for face anti spoofing")
  Member "joint 3d face reconstruction and dense face alignment from a single image with 2d assisted self supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "defending from adversarial examples with a two stream architecture")
  Member "gans trained by a two time scale update rule converge to a nash equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "stargan unified generative adversarial networks for multi domain image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "high resolution image synthesis and semantic manipulation with conditional gans":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)