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 as domain adaptation algorithm and analysis"):
  Abstract: To recognize the unseen classes with only few samples, few-shot learning (FSL) uses prior knowledge learned from the seen classes. A major challenge for FSL is that the distribution of the unseen classes is different from that of those seen, resulting in poor generalization even when a model is meta-trained on the seen classes. This class-difference-caused distribution shift can be considered as a special case of domain shift. In this paper, for the first time, we propose a domain adaptation prototypical network with attention (DAPNA) to explicitly tackle such a domain shift problem in a meta-learning framework. Specifically, armed with a set transformer based attention module, we construct each episode with two sub-episodes without class overlap on the seen classes to simulate the domain shift between the seen and unseen classes. To align the feature distributions of the two sub-episodes with limited training samples, a feature transfer network is employed together with a margin disparity discrepancy (MDD) loss. Importantly, theoretical analysis is provided to give the learning bound of our DAPNA. Extensive experiments show that our DAPNA outperforms the state-of-the-art FSL alternatives, often by significant margins.

Includes members:
  Member "cross domain few shot classification via learned feature wise transformation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learning across meta tasks for few shot learning")
  Member "charting the right manifold manifold mixup for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploiting unsupervised inputs for accurate few shot classification")
  Member "edge labeling graph neural network for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers")
  Member "generating classification weights with gnn denoising autoencoders for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "incremental few shot object detection")
  Member "bridging theory and algorithm for domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "a closer look at few shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting meta learning as supervised learning")
  Member "adaptive cross modal few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification")
  Member "set transformer a framework for attention based permutation invariant neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "set structured latent representations")

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.LG(Machine Learning)