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 to inpaint by progressively growing the mask regions"):
  Abstract: Image inpainting is one of the most challenging tasks in computer vision. Recently, generative-based image inpainting methods have been shown to produce visually plausible images. However, they still have difficulties to generate the correct structures and colors as the masked region grows large. This drawback is due to the training stability issue of the generative models. This work introduces a new curriculum-style training approach in the context of image inpainting. The proposed method increases the masked region size progressively in training time, during test time the user gives variable size and multiple holes at arbitrary locations. Incorporating such an approach in GANs may stabilize the training and provides better color consistencies and captures object continuities. We validate our approach on the MSCOCO and CelebA datasets. We report qualitative and quantitative comparisons of our training approach in different models.

Includes members:
  Member "foreground aware image inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fine grained image to image transformation towards visual recognition")
  Member "competence based curriculum learning for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning from easy to complex adaptive multi curricula learning for neural dialogue generation")
  Member "curriculum learning by transfer learning theory and experiments with deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey")
  Member "improved training with curriculum gans":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a u net based discriminator for generative adversarial networks")
  Member "free form image inpainting with gated convolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "a style based generator architecture for generative 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 "generative image inpainting with contextual attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "curriculum learning and minibatch bucketing in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "curriculum learning strategies for ir an empirical study on conversation response ranking")

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)