human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "learning structure via consensus for face segmentation and parsing"):
  with members:
    - "fsgan subject agnostic face swapping and reenactment"
    - "does generative face completion help face recognition"
    - "face mask extraction in video sequence"
    - "learning discriminators as energy networks in adversarial learning"
    - "adversarial structure matching loss for image segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "face completion with semantic knowledge and collaborative adversarial learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "recurrent convolutional strategies for face manipulation detection in videos"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "aird adversarial learning framework for image repurposing detection"
    - "mesonet a compact facial video forgery detection network"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "two stream neural networks for tampered face detection"
    - "deep multimodal image repurposing detection"
    - "action tubelet detector for spatio temporal action localization"

Connected through hyperedge HE-3 (induced by source paper "face parsing with roi tanh warping"):
  with members:
    - "integrated face analytics networks through cross dataset hybrid training"
    - "face parsing via a fully convolutional continuous crf neural network"
    - "generative face completion"
    - "attention is all you need"
    - "end to end semantic face segmentation with conditional random fields as convolutional recurrent and adversarial networks"
    - "mask r cnn"
    - "boundary aware instance segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "a high efficiency framework for constructing large scale face parsing benchmark"):
  with members:
    - "devil in the details towards accurate single and multiple human parsing"
    - "look at boundary a boundary aware face alignment algorithm"
    - "style aggregated network for facial landmark detection"
    - "synthesis of high quality visible faces from polarimetric thermal faces using generative adversarial networks"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "stacked hourglass networks for human pose estimation"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "part level car parsing and reconstruction from single street view"):
  with members:
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "instance level human parsing via part grouping network"
    - "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "3d pose estimation for fine grained object categories"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "improving facial attribute prediction using semantic segmentation"
    - "adversarial discriminative domain adaptation"

Question: Please tell me which class the center node 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)