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 "image deformation meta networks for one shot learning"):
  Abstract: Humans can robustly learn novel visual concepts even when images undergo various deformations and lose certain information. Mimicking the same behavior and synthesizing deformed instances of new concepts may help visual recognition systems perform better one-shot learning, i.e., learning concepts from one or few examples. Our key insight is that, while the deformed images may not be visually realistic, they still maintain critical semantic information and contribute significantly to formulating classifier decision boundaries. Inspired by the recent progress of meta-learning, we combine a meta-learner with an image deformation sub-network that produces additional training examples, and optimize both models in an end-to-end manner. The deformation sub-network learns to deform images by fusing a pair of images --- a probe image that keeps the visual content and a gallery image that diversifies the deformations. We demonstrate results on the widely used one-shot learning benchmarks (miniImageNet and ImageNet 1K Challenge datasets), which significantly outperform state-of-the-art approaches. Code is available at this https URL.

Includes members:
  Member "multi level semantic feature augmentation for one shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification")
  Member "low shot learning via covariance preserving adversarial augmentation networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "billion scale semi supervised learning for image classification")
  Member "delta encoder an effective sample synthesis method for few shot object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification")
  Member "deep meta learning learning to learn in the concept space":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification")
  Member "low shot learning from imaginary data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revisiting meta learning as supervised learning")
  Member "memory matching networks for one shot image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "continual local replacement for few shot learning")
  Member "dynamic few shot visual learning without forgetting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning representations by predicting bags of visual words")
  Member "prototypical networks for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine 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)