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: "a cnn cascade for landmark guided semantic part segmentation"
  Abstract: This paper proposes a CNN cascade for semantic part segmentation guided by pose-specific information encoded in terms of a set of landmarks (or keypoints). There is large amount of prior work on each of these tasks separately, yet, to the best of our knowledge, this is the first time in literature that the interplay between pose estimation and semantic part segmentation is investigated. To address this limitation of prior work, in this paper, we propose a CNN cascade of tasks that firstly performs landmark localisation and then uses this information as input for guiding semantic part segmentation. We applied our architecture to the problem of facial part segmentation and report large performance improvement over the standard unguided network on the most challenging face datasets. Testing code and models will be published online at this http URL.

Connected through hyperedge HE-1 (induced by source paper "end to end face parsing via interlinked convolutional neural networks"):
  with members:
    - "face parsing with roi tanh warping"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "face parsing via a fully convolutional continuous crf neural network"
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "mask r cnn"
    - "face alignment in the wild a survey"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (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"
    - "face parsing via recurrent propagation"
    - "mask r cnn"
    - "boundary aware instance segmentation"

Connected through hyperedge HE-3 (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"
    - "face parsing via recurrent propagation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised caricature face parsing through domain adaptation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"
    - "demystifying neural style transfer"
    - "diversified texture synthesis with feed forward networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-5 (induced by source paper "cnn cascades for segmenting whole slide images of the kidney"):
  with members:
    - "cutting out the middleman measuring nuclear area in histopathology slides without segmentation"
    - "understanding data augmentation for classification when to warp"
    - "instance aware semantic segmentation via multi task network cascades"
    - "u net convolutional networks for biomedical image segmentation"
    - "segmental hog new descriptor for glomerulus detection in kidney microscopy image"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)