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: "few shot segmentation propagation with guided networks"
  Abstract: Learning-based methods for visual segmentation have made progress on particular types of segmentation tasks, but are limited by the necessary supervision, the narrow definitions of fixed tasks, and the lack of control during inference for correcting errors. To remedy the rigidity and annotation burden of standard approaches, we address the problem of few-shot segmentation: given few image and few pixel supervision, segment any images accordingly. We propose guided networks, which extract a latent task representation from any amount of supervision, and optimize our architecture end-to-end for fast, accurate few-shot segmentation. Our method can switch tasks without further optimization and quickly update when given more guidance. We report the first results for segmentation from one pixel per concept and show real-time interactive video segmentation. Our unified approach propagates pixel annotations across space for interactive segmentation, across time for video segmentation, and across scenes for semantic segmentation. Our guided segmentor is state-of-the-art in accuracy for the amount of annotation and time. See this http URL for code, models, and more details.

Connected through hyperedge HE-1 (induced by source paper "training object detectors from few weakly labeled and many unlabeled images"):
  with members:
    - "weakly supervised object detection with segmentation collaboration"
    - "object aware instance labeling for weakly supervised object detection"
    - "few shot object detection with attention rpn and multi relation detector"
    - "few example object detection with model communication"
    - "feature weighting and boosting for few shot segmentation"
    - "label propagation for deep semi supervised learning"
    - "adaptive masked proxies for few shot segmentation"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-2 (induced by source paper "a novel bilevel paradigm for image to image translation"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "every smile is unique landmark guided diverse smile generation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "taskonomy disentangling task transfer learning"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-3 (induced by source paper "few shot viewpoint estimation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "on first order meta learning algorithms"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "prototypical networks for few shot learning"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"

Connected through hyperedge HE-4 (induced by source paper "few shot adaptive gaze estimation"):
  with members:
    - "evaluation of appearance based methods and implications for gaze based applications"
    - "on first order meta learning algorithms"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "light weight head pose invariant gaze tracking"
    - "deep pictorial gaze estimation"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "self normalizing neural networks"

Connected through hyperedge HE-5 (induced by source paper "feature weighting and boosting for few shot segmentation"):
  with members:
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "delta encoder an effective sample synthesis method for few shot object recognition"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "low shot learning from imaginary data"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "data augmentation using learned transformations for one shot medical image segmentation"):
  with members:
    - "voxelmorph a learning framework for deformable medical image registration"
    - "nonparametric density flows for mri intensity normalisation"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "autoaugment learning augmentation policies from data"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "an unsupervised learning model for deformable medical image registration"
    - "error corrective boosting for learning fully convolutional networks with limited data"
    - "one shot learning for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "fss 1000 a 1000 class dataset for few shot segmentation"):
  with members:
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "focal loss for dense object detection"
    - "prototypical networks for few shot learning"
    - "one shot learning for semantic segmentation"
    - "mask r cnn"
    - "learning feed forward one shot learners"
    - "matching networks for one shot learning"

Connected through hyperedge HE-8 (induced by source paper "panet few shot image semantic segmentation with prototype alignment"):
  with members:
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "prototypical networks for few shot learning"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "learning to compare relation network for few shot learning"
    - "one shot learning for semantic segmentation"
    - "refinenet multi path refinement networks for high resolution 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)