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 "dense classification and implanting for few shot learning"):
  Abstract: Training deep neural networks from few examples is a highly challenging and key problem for many computer vision tasks. In this context, we are targeting knowledge transfer from a set with abundant data to other sets with few available examples. We propose two simple and effective solutions: (i) dense classification over feature maps, which for the first time studies local activations in the domain of few-shot learning, and (ii) implanting, that is, attaching new neurons to a previously trained network to learn new, task-specific features. On miniImageNet, we improve the prior state-of-the-art on few-shot classification, i.e., we achieve 62.5%, 79.8% and 83.8% on 5-way 1-shot, 5-shot and 10-shot settings respectively.

Includes members:
  Member "fix your classifier the marginal value of training the last weight layer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards learning of filter level heterogeneous compression of convolutional neural networks")
  Member "rethinking feature distribution for loss functions in image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the devil is in the channels mutual channel loss for fine grained image classification")
  Member "tadam task dependent adaptive metric for improved few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to compare relation semantic alignment for few shot learning")
  Member "piggyback adapting a single network to multiple tasks by learning to mask weights":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "few shot few shot learning and the role of spatial attention")
  Member "ring loss convex feature normalization for face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an open set recognition and few shot learning dataset for audio event classification in domestic environments")
  Member "path aggregation network for instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "low shot learning from imaginary data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting meta learning as supervised learning")
  Member "dynamic few shot visual learning without forgetting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning representations by predicting bags of visual words")

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)