human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "regularization for unsupervised deep neural nets"
  Abstract: Unsupervised neural networks, such as restricted Boltzmann machines (RBMs) and deep belief networks (DBNs), are powerful tools for feature selection and pattern recognition tasks. We demonstrate that overfitting occurs in such models just as in deep feedforward neural networks, and discuss possible regularization methods to reduce overfitting. We also propose a "partial" approach to improve the efficiency of Dropout/DropConnect in this scenario, and discuss the theoretical justification of these methods from model convergence and likelihood bounds. Finally, we compare the performance of these methods based on their likelihood and classification error rates for various pattern recognition data sets.

Connected through hyperedge HE-1 (induced by source paper "a preliminary study on data augmentation of deep learning for image classification"):
  with members:
    - "deepgini prioritizing massive tests to reduce labeling cost"
    - "neuralvis visualizing and interpreting deep learning models"
    - "autoaugment learning augmentation policies from data"
    - "recent advances in recurrent neural networks"
    - "improving deep learning using generic data augmentation"
    - "random erasing data augmentation"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-2 (induced by source paper "automated image data preprocessing with deep reinforcement learning"):
  with members:
    - "diminishing batch normalization"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "active object localization with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "the effectiveness of data augmentation in image classification using deep learning"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "recurrent topic transition gan for visual paragraph generation"
    - "understanding data augmentation for classification when to warp"
    - "deep big simple neural nets for handwritten digit recognition"

Question: Please tell me which class the center node 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.LG(Machine Learning)