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 "a new benchmark for evaluation of cross domain few shot learning"):
  Abstract: Recent progress on few-shot learning has largely re-lied on annotated data for meta-learning, sampled from the same domain as the novel classes. However, in many applications, collecting data for meta-learning is infeasible or impossible. This leads to the cross-domain few-shot learn-ing problem, where a large domain shift exists between base and novel classes. Although some preliminary investigation of the few-shot methods under domain shift exists, a standard benchmark for cross-domain few-shot learning is not yet established. In this paper, we propose the cross-domain few-shot learning (CD-FSL) benchmark, consist-ing of images from diverse domains with varying similarity to ImageNet, ranging from crop disease images, satellite images, and medical images. Extensive experiments on the proposed benchmark are performed to compare an array of state-of-art meta-learning and transfer learning approaches, including various forms of single model fine-tuning and ensemble learning. The results demonstrate that current meta-learning methods underperform in relation to simple fine-tuning by 12.8% average accuracy. Accuracy of all methods tend to correlate with dataset similarity toImageNet. In addition, the relative performance gain with increasing number of shots is greater with transfer methods compared to meta-learning. Finally, we demonstrate that transferring from multiple pretrained models achieves best performance, with accuracy improvements of 14.9% and 1.9% versus the best of meta-learning and single model fine-tuning approaches, respectively. In summary, the proposed benchmark serves as a challenging platform to guide future research on cross-domain few-shot learning due to its spectrum of diversity and coverage

Includes members:
  Member "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "practical and bilateral privacy preserving federated learning")
  Member "meta learning with differentiable convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  Member "diversity with cooperation ensemble methods for few shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards fair cross domain adaptation via generative learning")
  Member "a closer look at few shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting meta learning as supervised learning")
  Member "meta dataset a dataset of datasets for learning to learn from few examples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "on first order meta learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "do better imagenet models transfer better":
    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")
  Member "meta learning for semi supervised few shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")

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)