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 "enhancing feature invariance with learned image transformations for image retrieval"):
  Abstract: Off-the-shelf convolutional neural network features achieve state-of-the-art results in many image retrieval tasks. However, their invariance is pre-defined by the network architecture and training data. In this work, we propose using features aggregated from transformed images to increase the invariance of off-the-shelf features without fine-tuning or modifying the network. We learn an ensemble of beneficial image transformations through reinforcement learning in an efficient way. Experiment results show the learned ensemble of transformations is effective and transferable.

Includes members:
  Member "component based attention for large scale trademark retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mtrnet a generic scene text eraser")
  Member "learning transferable architectures for scalable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning curiosity algorithms")
  Member "deformable convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "fine tuning cnn image retrieval with no human annotation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "a large scale dataset and benchmark for similar trademark retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "component based attention for large scale trademark retrieval")
  Member "proximal policy optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "learning rotation invariant convolutional filters for texture classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rest performance improvement of a black box model via rl based spatial transformation")
  Member "cross dimensional weighting for aggregated deep convolutional features":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "constrained dominant sets and its applications in computer vision")

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)