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 "meta learning across meta tasks for few shot learning"):
  Abstract: Existing meta-learning based few-shot learning (FSL) methods typically adopt an episodic training strategy whereby each episode contains a meta-task. Across episodes, these tasks are sampled randomly and their relationships are ignored. In this paper, we argue that the inter-meta-task relationships should be exploited to learn models that are more generalizable to unseen classes with few-shots. Specifically, we consider the relationships between two types of meta-tasks and propose different strategies to exploit them. (1) Two meta-tasks with disjoint sets of classes: these are interesting because their relationship is reminiscent of that between the source seen classes and target unseen classes, featured with domain gap caused by class differences. A novel meta-training strategy named meta-domain adaptation (MDA) is proposed to make the meta-learned model more robust to the domain gap. (2) Two meta-tasks with identical sets of classes: these are interesting because they can be used to learn models that are robust against poorly sampled few-shots. To that end, a novel meta-knowledge distillation (MKD) strategy is formulated. Extensive experiments demonstrate that both MDA and MKD significantly boost the performance of a variety of existing FSL methods and thus achieve new state-of-the-art on three benchmarks.

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 "few shot learning as domain adaptation algorithm and analysis")
  Member "learning what and where to transfer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "context aware domain adaptation in semantic segmentation")
  Member "charting the right manifold manifold mixup for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploiting unsupervised inputs for accurate few shot classification")
  Member "infinite mixture prototypes for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adargcn adaptive aggregation gcn for few shot learning")
  Member "on minimum discrepancy estimation for deep domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "impact of imagenet model selection on domain adaptation")
  Member "generating classification weights with gnn denoising autoencoders for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "incremental few shot object detection")
  Member "bridging theory and algorithm for domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "transductive episodic wise adaptive metric for few shot learning":
    also connected through other hyperedges:
      (none)

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)