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: "semantic part segmentation using compositional model combining shape and appearance"
  Abstract: In this paper, we study the problem of semantic part segmentation for animals. This is more challenging than standard object detection, object segmentation and pose estimation tasks because semantic parts of animals often have similar appearance and highly varying shapes. To tackle these challenges, we build a mixture of compositional models to represent the object boundary and the boundaries of semantic parts. And we incorporate edge, appearance, and semantic part cues into the compositional model. Given part-level segmentation annotation, we develop a novel algorithm to learn a mixture of compositional models under various poses and viewpoints for certain animal classes. Furthermore, a linear complexity algorithm is offered for efficient inference of the compositional model using dynamic programming. We evaluate our method for horse and cow using a newly annotated dataset on Pascal VOC 2010 which has pixelwise part labels. Experimental results demonstrate the effectiveness of our method.

Connected through hyperedge HE-1 (induced by source paper "deep portrait image completion and extrapolation"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "everybody dance now"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "generative face completion"
    - "a generative model of people in clothing"

Connected through hyperedge HE-2 (induced by source paper "selective segmentation networks using top down attention"):
  with members:
    - "stnet selective tuning of convolutional networks for object localization"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "ssd single shot multibox detector"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "attention to scale scale aware semantic image segmentation"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "deep nets what have they ever done for vision"):
  with members:
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "the elephant in the room"
    - "deep learning a critical appraisal"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "progressive neural architecture search"
    - "few shot image recognition by predicting parameters from activations"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-4 (induced by source paper "look into person joint body parsing pose estimation network and a new benchmark"):
  with members:
    - "active self paced learning for cost effective and progressive face identification"
    - "multi context attention for human pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "human pose estimation via convolutional part heatmap regression"
    - "proposal free network for instance level object segmentation"
    - "semantic object parsing with local global long short term memory"

Connected through hyperedge HE-5 (induced by source paper "weakly and semi supervised human body part parsing via pose guided knowledge transfer"):
  with members:
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "interpretable structure evolving lstm"
    - "joint multi person pose estimation and semantic part segmentation"
    - "semantic object parsing with graph lstm"
    - "stacked hourglass networks for human pose estimation"
    - "rmpe regional multi person pose estimation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "semantic video segmentation a review on recent approaches"):
  with members:
    - "stfcn spatio temporal fcn for semantic video segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "std2p rgbd semantic segmentation using spatio temporal data driven pooling"
    - "optical flow with semantic segmentation and localized layers"
    - "convolutional random walk networks for semantic image segmentation"
    - "semantic image segmentation via deep parsing network"
    - "deep hierarchical parsing for semantic segmentation"
    - "attention to scale scale aware semantic image segmentation"

Connected through hyperedge HE-7 (induced by source paper "deep feature factorization for concept discovery"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "methods for interpreting and understanding deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "co localization with category consistent features and geodesic distance propagation"
    - "image co localization by mimicking a good detector s confidence score distribution"
    - "why should i trust you explaining the predictions of any classifier"
    - "do semantic parts emerge in convolutional neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "creatures great and smal recovering the shape and motion of animals from video"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "video object segmentation with re identification"
    - "end to end recovery of human shape and pose"
    - "silnet single and multi view reconstruction by learning from silhouettes"
    - "stacked hourglass networks for human pose estimation"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "3d menagerie modeling the 3d shape and pose of animals"
    - "human pose estimation via convolutional part heatmap regression"

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)