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 "prototype rectification for few shot learning"):
  Abstract: Few-shot learning is a challenging problem that requires a model to recognize novel classes with few labeled data. In this paper, we aim to find the expected prototypes of the novel classes, which have the maximum cosine similarity with the samples of the same class. Firstly, we propose a cosine similarity based prototypical network to compute basic prototypes of the novel classes from the few samples. A bias diminishing module is further proposed for prototype rectification since the basic prototypes computed in the low-data regime are biased against the expected prototypes. In our method, the intra-class bias and the cross-class bias are diminished to modify the prototypes. Then we give a theoretical analysis of the impact of the bias diminishing module on the expected performance of our method. We conduct extensive experiments on four few-shot benchmarks and further analyze the advantage of the bias diminishing module. The bias diminishing module brings in significant improvement by a large margin of 3% to 9% in general. Notably, our approach achieves state-of-the-art performance on miniImageNet (70.31% in 1-shot and 81.89% in 5-shot) and tieredImageNet (78.74% in 1-shot and 86.92% in 5-shot), which demonstrates the superiority of the proposed method.

Includes members:
  Member "a baseline for few shot image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimized generic feature learning for few shot classification across domains")
  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 "boosting few shot visual learning with self supervision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning representations by predicting bags of visual words")
  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 "edge labeling graph neural network 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 "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 "on first order meta learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "meta learning with latent embedding optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting meta learning as supervised 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)