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: "progressive learning for systematic design of large neural networks"
  Abstract: We develop an algorithm for systematic design of a large artificial neural network using a progression property. We find that some non-linear functions, such as the rectifier linear unit and its derivatives, hold the property. The systematic design addresses the choice of network size and regularization of parameters. The number of nodes and layers in network increases in progression with the objective of consistently reducing an appropriate cost. Each layer is optimized at a time, where appropriate parameters are learned using convex optimization. Regularization parameters for convex optimization do not need a significant manual effort for tuning. We also use random instances for some weight matrices, and that helps to reduce the number of parameters we learn. The developed network is expected to show good generalization power due to appropriate regularization and use of random weights in the layers. This expectation is verified by extensive experiments for classification and regression problems, using standard databases.

Connected through hyperedge HE-1 (induced by source paper "heterogeneous multilayer generalized operational perceptron"):
  with members:
    - "improving efficiency in convolutional neural networks with multilinear filters"
    - "layer wise training of deep networks using kernel similarity"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a survey of model compression and acceleration for deep neural networks"
    - "wide residual networks"
    - "neural architecture search with reinforcement learning"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "ssfn self size estimating feed forward network with low complexity limited need for human intervention and consistent behaviour across trials"):
  with members:
    - "do cifar 10 classifiers generalize to cifar 10"
    - "autoaugment learning augmentation policies from data"
    - "layer wise training of deep networks using kernel similarity"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "subset sampling for progressive neural network learning"):
  with members:
    - "heterogeneous multilayer generalized operational perceptron"
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "data driven neural architecture learning for financial time series forecasting"
    - "progressive operational perceptron with memory"
    - "are all training examples created equal an empirical study"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "data driven neural architecture learning for financial time series forecasting"):
  with members:
    - "heterogeneous multilayer generalized operational perceptron"
    - "progressive operational perceptron with memory"
    - "benchmark dataset for mid price prediction of limit order book data"
    - "multilinear class specific discriminant analysis"
    - "tensor representation in high frequency financial data for price change prediction"
    - "neural architecture search with reinforcement learning"
    - "predicting the direction of stock market prices using random forest"

Connected through hyperedge HE-5 (induced by source paper "progressive operational perceptron with memory"):
  with members:
    - "heterogeneous multilayer generalized operational perceptron"
    - "data driven neural architecture learning for financial time series forecasting"
    - "universal approximation with quadratic deep networks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image 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.NE(Neural and Evolutionary Computing)