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: "object level representation learning for few shot image classification"
  Abstract: Few-shot learning that trains image classifiers over few labeled examples per category is a challenging task. In this paper, we propose to exploit an additional big dataset with different categories to improve the accuracy of few-shot learning over our target dataset. Our approach is based on the observation that images can be decomposed into objects, which may appear in images from both the additional dataset and our target dataset. We use the object-level relation learned from the additional dataset to infer the similarity of images in our target dataset with unseen categories. Nearest neighbor search is applied to do image classification, which is a non-parametric model and thus does not need fine-tuning. We evaluate our algorithm on two popular datasets, namely Omniglot and MiniImagenet. We obtain 8.5\% and 2.7\% absolute improvements for 5-way 1-shot and 5-way 5-shot experiments on MiniImagenet, respectively. Source code will be published upon acceptance.

Connected through hyperedge HE-1 (induced by source paper "revisiting metric learning for few shot image classification"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "multi level semantic feature augmentation for one shot learning"
    - "meta learning with differentiable convex optimization"
    - "lgm net learning to generate matching networks for few shot learning"
    - "few shot learning with localization in realistic settings"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "edge labeling graph neural network for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"

Connected through hyperedge HE-2 (induced by source paper "leveraging pretrained image classifiers for language based segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to exploit the prior network knowledge for weakly supervised semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "rtseg real time semantic segmentation comparative study"
    - "taskonomy disentangling task transfer learning"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "real time joint semantic segmentation and depth estimation using asymmetric annotations"
    - "decoupled spatial neural attention for weakly supervised semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "efficient automatic meta optimization search for few shot learning"):
  with members:
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "regularized evolution for image classifier architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"
    - "learning to compare relation network for few shot learning"
    - "hyperparameter optimization a spectral approach"

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)