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: "one shot learning for semantic segmentation"
  Abstract: Low-shot learning methods for image classification support learning from sparse data. We extend these techniques to support dense semantic image segmentation. Specifically, we train a network that, given a small set of annotated images, produces parameters for a Fully Convolutional Network (FCN). We use this FCN to perform dense pixel-level prediction on a test image for the new semantic class. Our architecture shows a 25% relative meanIoU improvement compared to the best baseline methods for one-shot segmentation on unseen classes in the PASCAL VOC 2012 dataset and is at least 3 times faster.

Connected through hyperedge HE-1 (induced by source paper "cafenet class agnostic few shot edge detection network"):
  with members:
    - "transductive few shot learning with meta learned confidence"
    - "dense classification and implanting for few shot learning"
    - "bi directional cascade network for perceptual edge detection"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "fss 1000 a 1000 class dataset for few shot segmentation"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "dynamic feature fusion for semantic edge detection"
    - "panet few shot image semantic segmentation with prototype alignment"

Connected through hyperedge HE-2 (induced by source paper "on the texture bias for few shot cnn segmentation"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "a new local transformation module for few shot segmentation"
    - "meta learning initializations for image segmentation"
    - "fss 1000 a 1000 class dataset for few shot segmentation"
    - "feature weighting and boosting for few shot segmentation"
    - "texture bias of cnns limits few shot classification performance"
    - "adaptive masked proxies for few shot segmentation"
    - "panet few shot image semantic segmentation with prototype alignment"

Connected through hyperedge HE-3 (induced by source paper "lt net label transfer by learning reversible voxel wise correspondence for one shot medical image segmentation"):
  with members:
    - "bridging stereo matching and optical flow via spatiotemporal correspondence"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "one shot neural architecture search via self evaluated template network"
    - "fully convolutional one shot object segmentation for industrial robotics"
    - "learning correspondence from the cycle consistency of time"
    - "votenet a deep learning label fusion method for multi atlas segmentation"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "one shot neural architecture search through a posteriori distribution guided sampling"

Connected through hyperedge HE-4 (induced by source paper "prototype refinement network for few shot segmentation"):
  with members:
    - "a baseline for few shot image classification"
    - "adaptive masked proxies for few shot segmentation"
    - "panet few shot image semantic segmentation with prototype alignment"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "reptile a scalable metalearning algorithm"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised few shot object segmentation using co attention with visual and semantic inputs"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "one shot object detection with co attention and co excitation"
    - "online object representations with contrastive learning"
    - "adaptive masked proxies for few shot segmentation"
    - "panet few shot image semantic segmentation with prototype alignment"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "youtube vos a large scale video object segmentation benchmark"
    - "online object and task learning via human robot interaction"

Connected through hyperedge HE-6 (induced by source paper "multi level semantic feature augmentation for one shot learning"):
  with members:
    - "deep meta learning learning to learn in the concept space"
    - "low shot learning from imaginary data"
    - "memory matching networks for one shot image recognition"
    - "dynamic few shot visual learning without forgetting"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "meta sgd learning to learn quickly for few shot learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "a deep one shot network for query based logo retrieval"):
  with members:
    - "script identification in natural scene image and video frames using an attention based convolutional lstm network"
    - "handwriting recognition in low resource scripts using adversarial learning"
    - "texture synthesis guided deep hashing for texture image retrieval"
    - "multi scale location aware kernel representation for object detection"
    - "open logo detection challenge"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "scalable deep learning logo detection"
    - "focal loss for dense object detection"

Connected through hyperedge HE-8 (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"
    - "few shot segmentation propagation with guided networks"
    - "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"

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)