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: "deep learning for semantic part segmentation with high level guidance"
  Abstract: In this work we address the task of segmenting an object into its parts, or semantic part segmentation. We start by adapting a state-of-the-art semantic segmentation system to this task, and show that a combination of a fully-convolutional Deep CNN system coupled with Dense CRF labelling provides excellent results for a broad range of object categories. Still, this approach remains agnostic to high-level constraints between object parts. We introduce such prior information by means of the Restricted Boltzmann Machine, adapted to our task and train our model in an discriminative fashion, as a hidden CRF, demonstrating that prior information can yield additional improvements. We also investigate the performance of our approach ``in the wild'', without information concerning the objects' bounding boxes, using an object detector to guide a multi-scale segmentation scheme. We evaluate the performance of our approach on the Penn-Fudan and LFW datasets for the tasks of pedestrian parsing and face labelling respectively. We show superior performance with respect to competitive methods that have been extensively engineered on these benchmarks, as well as realistic qualitative results on part segmentation, even for occluded or deformable objects. We also provide quantitative and extensive qualitative results on three classes from the PASCAL Parts dataset. Finally, we show that our multi-scale segmentation scheme can boost accuracy, recovering segmentations for finer parts.

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 "i bet you are wrong gambling adversarial networks for structured semantic segmentation"):
  with members:
    - "understanding deep learning techniques for image segmentation"
    - "adversarial structure matching loss for image segmentation"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "student beats the teacher deep neural networks for lateral ventricles segmentation in brain mr"
    - "efficient relaxations for dense crfs with sparse higher order potentials"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "adversarial learning for semi supervised semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "structured output learning with conditional generative flows"):
  with members:
    - "macow masked convolutional generative flow"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "emerging convolutions for generative normalizing flows"
    - "neural autoregressive flows"
    - "deep structured prediction with nonlinear output transformations"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "learning approximate inference networks for structured prediction"

Connected through hyperedge HE-4 (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-5 (induced by source paper "keypoint based weakly supervised human parsing"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "m2e try on net fashion from model to everyone"
    - "multiple human parsing in the wild"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "joint multi person pose estimation and semantic part segmentation"
    - "mask r cnn"
    - "rmpe regional multi person pose estimation"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "densereg fully convolutional dense shape regression in the wild"):
  with members:
    - "unite the people closing the loop between 3d and 2d human representations"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "two stage convolutional part heatmap regression for the 1st 3d face alignment in the wild 3dfaw challenge"
    - "human pose estimation via convolutional part heatmap regression"
    - "supervised transformer network for efficient face detection"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "dense human body correspondences using convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "deep value networks learn to evaluate and iteratively refine structured outputs"):
  with members:
    - "pixel recursive super resolution"
    - "perceptual losses for real time style transfer and super resolution"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "conditional image generation with pixelcnn decoders"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a learned representation for artistic style"
    - "input convex neural networks"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-8 (induced by source paper "illuminating pedestrians via simultaneous detection segmentation"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "how far are we from solving pedestrian detection"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "fused dnn a deep neural network fusion approach to fast and robust pedestrian detection"
    - "is faster r cnn doing well for pedestrian detection"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "scale aware fast r cnn for pedestrian detection"
    - "instance aware semantic segmentation via multi task network cascades"

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)