human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "few shot learning with weakly supervised object localization"):
  Abstract: Few-shot learning (FSL) aims to learn novel visual categories from very few samples, which is a challenging problem in real-world applications. Many data generation methods have improved the performance of FSL models, but require lots of annotated images to train a specialized network (e.g., GAN) dedicated to hallucinate new samples. We argue that localization is a more efficient approach because it provides the most discriminative regions without using extra samples. In this paper, we propose a novel method to address the FSL task by achieving weakly-supervised object localization within performing few-shot classification. To this end, we design (i) a triplet-input module to obtain the initial object seeds and (ii) an Image-To-Class-Distance (ITCD) based localizer to activate the deep descriptors of the key objects, thus obtaining the more discriminative representations used to perform few-shot classification. Extensive experiments show our method outperforms the state-of-the-art methods on benchmark datasets under various settings. Besides, our method achieves superior performance over previous methods when training the model on miniImageNet and evaluating it on the different datasets (e.g., Stanford Dogs), demonstrating its superior generalization capacity. Extra visualization shows the proposed method can localize the key objects accurately.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "baby steps towards few shot learning with multiple semantics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification")
  Member "few shot learning for domain specfic fine grained image classfication":
    also connected through other hyperedges:
      (none)
  Member "few shot learning via saliency guided hallucination of samples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "incremental few shot object detection")
  Member "few shot learning with localization in realistic settings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers")
  Member "revisiting local descriptor based image to class measure for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers")
  Member "laso label set operations networks for multi label few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification")
  Member "do imagenet classifiers generalize to imagenet":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")

Question: Please tell me which class the source paper 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)