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 "low shot learning from imaginary 3d model"):
  Abstract: Since the advent of deep learning, neural networks have demonstrated remarkable results in many visual recognition tasks, constantly pushing the limits. However, the state-of-the-art approaches are largely unsuitable in scarce data regimes. To address this shortcoming, this paper proposes employing a 3D model, which is derived from training images. Such a model can then be used to hallucinate novel viewpoints and poses for the scarce samples of the few-shot learning scenario. A self-paced learning approach allows for the selection of a diverse set of high-quality images, which facilitates the training of a classifier. The performance of the proposed approach is showcased on the fine-grained CUB-200-2011 dataset in a few-shot setting and significantly improves our baseline accuracy.

Includes members:
  Member "self paced deep learning for weakly supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low budget unsupervised label query through domain alignment enforcement")
  Member "learning category specific mesh reconstruction from image collections":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "low shot learning from imaginary data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting meta learning as supervised learning")
  Member "low shot learning with large scale diffusion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semi supervised few shot learning with prototypical random walks")
  Member "prototypical networks for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "few example object detection with model communication")
  Member "keep it smpl automatic estimation of 3d human pose and shape from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "learning feed forward one shot learners":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a two stage approach to few shot learning for image recognition")

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)