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: "adaptive masked proxies for few shot segmentation"
  Abstract: Deep learning has thrived by training on large-scale datasets. However, in robotics applications sample efficiency is critical. We propose a novel adaptive masked proxies method that constructs the final segmentation layer weights from few labelled samples. It utilizes multi-resolution average pooling on base embeddings masked with the label to act as a positive proxy for the new class, while fusing it with the previously learned class signatures. Our method is evaluated on PASCAL-$5^i$ dataset and outperforms the state-of-the-art in the few-shot semantic segmentation. Unlike previous methods, our approach does not require a second branch to estimate parameters or prototypes, which enables it to be used with 2-stream motion and appearance based segmentation networks. We further propose a novel setup for evaluating continual learning of object segmentation which we name incremental PASCAL (iPASCAL) where our method outperforms the baseline method. Our code is publicly available at this https URL.

Connected through hyperedge HE-1 (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"
    - "panet few shot image semantic segmentation with prototype alignment"
    - "differentiable meta learning model for few shot semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "prototype refinement network for few shot segmentation"):
  with members:
    - "a baseline for few shot image classification"
    - "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"
    - "one shot learning for semantic segmentation"

Connected through hyperedge HE-3 (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"
    - "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"
    - "one shot learning for semantic segmentation"

Connected through hyperedge HE-4 (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"
    - "mixmatch a holistic approach to semi supervised learning"
    - "learning semantic part based models from google images"

Connected through hyperedge HE-5 (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"
    - "panet few shot image semantic segmentation with prototype alignment"
    - "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"

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)