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: "panet few shot image semantic segmentation with prototype alignment"
  Abstract: Despite the great progress made by deep CNNs in image semantic segmentation, they typically require a large number of densely-annotated images for training and are difficult to generalize to unseen object categories. Few-shot segmentation has thus been developed to learn to perform segmentation from only a few annotated examples. In this paper, we tackle the challenging few-shot segmentation problem from a metric learning perspective and present PANet, a novel prototype alignment network to better utilize the information of the support set. Our PANet learns class-specific prototype representations from a few support images within an embedding space and then performs segmentation over the query images through matching each pixel to the learned prototypes. With non-parametric metric learning, PANet offers high-quality prototypes that are representative for each semantic class and meanwhile discriminative for different classes. Moreover, PANet introduces a prototype alignment regularization between support and query. With this, PANet fully exploits knowledge from the support and provides better generalization on few-shot segmentation. Significantly, our model achieves the mIoU score of 48.1% and 55.7% on PASCAL-5i for 1-shot and 5-shot settings respectively, surpassing the state-of-the-art method by 1.8% and 8.6%.

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"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"

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"
    - "differentiable meta learning model for few shot semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "semi supervised few shot learning for medical image segmentation"):
  with members:
    - "on the texture bias for few shot cnn segmentation"
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "boosting few shot visual learning with self supervision"
    - "fss 1000 a 1000 class dataset for few shot segmentation"
    - "feature weighting and boosting for few shot segmentation"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"

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"
    - "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"
    - "one shot learning for semantic segmentation"

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"
    - "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"
    - "one shot learning for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "one shot weakly supervised video object segmentation"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "adaptive masked proxies for few shot segmentation"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "video object segmentation with language referring expressions"
    - "youtube vos a large scale video object segmentation benchmark"
    - "premvos proposal generation refinement and merging for video object segmentation"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"

Connected through hyperedge HE-7 (induced by source paper "differentiable meta learning model for few shot semantic segmentation"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "provable guarantees for gradient based meta learning"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "meta learning with differentiable closed form solvers"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "prototypical networks for few shot learning"
    - "one shot learning for semantic segmentation"
    - "model agnostic meta learning for fast adaptation of deep networks"

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)