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: "fss 1000 a 1000 class dataset for few shot segmentation"
  Abstract: Over the past few years, we have witnessed the success of deep learning in image recognition thanks to the availability of large-scale human-annotated datasets such as PASCAL VOC, ImageNet, and COCO. Although these datasets have covered a wide range of object categories, there are still a significant number of objects that are not included. Can we perform the same task without a lot of human annotations? In this paper, we are interested in few-shot object segmentation where the number of annotated training examples are limited to 5 only. To evaluate and validate the performance of our approach, we have built a few-shot segmentation dataset, FSS-1000, which consists of 1000 object classes with pixelwise annotation of ground-truth segmentation. Unique in FSS-1000, our dataset contains significant number of objects that have never been seen or annotated in previous datasets, such as tiny daily objects, merchandise, cartoon characters, logos, etc. We build our baseline model using standard backbone networks such as VGG-16, ResNet-101, and Inception. To our surprise, we found that training our model from scratch using FSS-1000 achieves comparable and even better results than training with weights pre-trained by ImageNet which is more than 100 times larger than FSS-1000. Both our approach and dataset are simple, effective, and easily extensible to learn segmentation of new object classes given very few annotated training examples. Dataset is available at this https URL.

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"
    - "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"
    - "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"
    - "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"
    - "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"
    - "feature weighting and boosting 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"
    - "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 "meta learning initializations for image segmentation"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "on first order meta learning algorithms"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "reptile a scalable metalearning algorithm"
    - "meta learning with latent embedding optimization"

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)