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 "automated image data preprocessing with deep reinforcement learning"):
  Abstract: Data preparation, i.e. the process of transforming raw data into a format that can be used for training effective machine learning models, is a tedious and time-consuming task. For image data, preprocessing typically involves a sequence of basic transformations such as cropping, filtering, rotating or flipping images. Currently, data scientists decide manually based on their experience which transformations to apply in which particular order to a given image data set. Besides constituting a bottleneck in real-world data science projects, manual image data preprocessing may yield suboptimal results as data scientists need to rely on intuition or trial-and-error approaches when exploring the space of possible image transformations and thus might not be able to discover the most effective ones. To mitigate the inefficiency and potential ineffectiveness of manual data preprocessing, this paper proposes a deep reinforcement learning framework to automatically discover the optimal data preprocessing steps for training an image classifier. The framework takes as input sets of labeled images and predefined preprocessing transformations. It jointly learns the classifier and the optimal preprocessing transformations for individual images. Experimental results show that the proposed approach not only improves the accuracy of image classifiers, but also makes them substantially more robust to noisy inputs at test time.

Includes members:
  Member "diminishing batch normalization":
    also connected through other hyperedges:
      (none)
  Member "the effectiveness of data augmentation in image classification using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "regularization for unsupervised deep neural nets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a preliminary study on data augmentation of deep learning for image classification")
  Member "active object localization with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")

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)