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 "learning the non linearity in convolutional neural networks"):
  Abstract: We propose the introduction of nonlinear operation into the feature generation process in convolutional neural networks. This nonlinearity can be implemented in various ways. First we discuss the use of nonlinearities in the process of data augmentation to increase the robustness of the neural networks recognition capacity. To this end, we randomly disturb the input data set by applying exponents within a certain numerical range to individual data points of the input space. Second we propose nonlinear convolutional neural networks where we apply the exponential operation to each element of the receptive field. To this end, we define an additional weight matrix of the same dimension as the standard kernel weight matrix. The weights of this matrix then constitute the exponents of the corresponding components of the receptive field. In the basic setting, we keep the weight parameters fixed during training by defining suitable parameters. Alternatively, we make the exponential weight parameters end-to-end trainable using a suitable parameterization. The network architecture is applied to time series analysis data set showing a considerable increase in the classification performance compared to baseline networks.

Includes members:
  Member "kervolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph optimization approach to range based localization")
  Member "neural arithmetic logic units":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stealing black box functionality using the deep neural tree architecture")
  Member "convolutional neural networks with recurrent neural filters":
    also connected through other hyperedges:
      (none)
  Member "non linear convolution filters for cnn based learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "kervolutional neural networks")
  Member "splinecnn fast geometric deep learning with continuous b spline kernels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep graph matching consensus")
  Member "aggregated residual transformations for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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.LG(Machine Learning)