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: "transductive few shot learning with meta learned confidence"
  Abstract: We propose a novel transductive inference framework for metric-based meta-learning models, which updates the prototype of each class with the confidence-weighted average of all the support and query samples. However, a caveat here is that the model confidence may be unreliable, which could lead to incorrect prediction in the transductive setting. To tackle this issue, we further propose to meta-learn to assign correct confidence scores to unlabeled queries. Specifically, we meta-learn the parameters of the distance-metric, such that the model can improve its transductive inference performance on unseen tasks with the generated confidence scores. We also consider various types of uncertainties to further enhance the reliability of the meta-learned confidence. We combine our transductive meta-learning scheme, Meta-Confidence Transduction (MCT) with a novel dense classifier, Dense Feature Matching Network (DFMN), which performs both instance-level and feature-level classification without global average pooling and validate it on four benchmark datasets. Our model achieves state-of-the-art results on all datasets, outperforming existing state-of-the-art models by 11.11% and 7.68% on miniImageNet and tieredImageNet dataset respectively. Further qualitative analysis confirms that this impressive performance gain is indeed due to its ability to assign high confidence to instances with the correct labels.

Connected through hyperedge HE-1 (induced by source paper "cafenet class agnostic few shot edge detection network"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "bi directional cascade network for perceptual edge detection"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "fss 1000 a 1000 class dataset for few shot segmentation"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "dynamic feature fusion for semantic edge detection"
    - "panet few shot image semantic segmentation with prototype alignment"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"

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.LG(Machine Learning)