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 parsing via recurrent propagation"):
  Abstract: Face parsing is an important problem in computer vision that finds numerous applications including recognition and editing. Recently, deep convolutional neural networks (CNNs) have been applied to image parsing and segmentation with the state-of-the-art performance. In this paper, we propose a face parsing algorithm that combines hierarchical representations learned by a CNN, and accurate label propagations achieved by a spatially variant recurrent neural network (RNN). The RNN-based propagation approach enables efficient inference over a global space with the guidance of semantic edges generated by a local convolutional model. Since the convolutional architecture can be shallow and the spatial RNN can have few parameters, the framework is much faster and more light-weighted than the state-of-the-art CNNs for the same task. We apply the proposed model to coarse-grained and fine-grained face parsing. For fine-grained face parsing, we develop a two-stage approach by first identifying the main regions and then segmenting the detail components, which achieves better performance in terms of accuracy and efficiency. With a single GPU, the proposed algorithm parses face images accurately at 300 frames per second, which facilitates real-time applications.

Includes members:
  Member "semantic style transfer and turning two bit doodles into fine artworks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial camouflage hiding physical world attacks with natural styles")
  Member "pixel recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "semantic object parsing with local global long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "semantic image segmentation with task specific edge detection using cnns and a discriminatively trained domain transform":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cars can t fly up in the sky improving urban scene segmentation via height driven attention networks")
  Member "deep learning for semantic part segmentation with high level guidance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning structure via consensus for face segmentation and parsing")
  Member "grid long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "arma nets expanding receptive field for dense prediction")
  Member "renet a recurrent neural network based alternative to convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rnnpool efficient non linear pooling for ram constrained inference")
  Member "draw a recurrent neural network for image generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "twostreamvan improving motion modeling in video generation")

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)