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 operational perceptron with memory"
  Abstract: Generalized Operational Perceptron (GOP) was proposed to generalize the linear neuron model in the traditional Multilayer Perceptron (MLP) and this model can mimic the synaptic connections of the biological neurons that have nonlinear neurochemical behaviours. Progressive Operational Perceptron (POP) is a multilayer network composing of GOPs which is formed layer-wise progressively. In this work, we propose major modifications that can accelerate as well as augment the progressive learning procedure of POP by incorporating an information-preserving, linear projection path from the input to the output layer at each progressive step. The proposed extensions can be interpreted as a mechanism that provides direct information extracted from the previously learned layers to the network, hence the term "memory". This allows the network to learn deeper architectures with better data representations. An extensive set of experiments show that the proposed modifications can surpass the learning capability of the original POPs and other related algorithms.

Connected through hyperedge HE-1 (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"
    - "are all training examples created equal an empirical study"
    - "progressive learning for systematic design of large neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-2 (induced by source paper "operational neural networks"):
  with members:
    - "heterogeneous multilayer generalized operational perceptron"
    - "efficient neural architecture search via parameter sharing"
    - "layer wise training of deep networks using kernel similarity"
    - "learning transferable architectures for scalable image recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "semantic segmentation using adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "fully convolutional instance aware semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "data driven neural architecture learning for financial time series forecasting"):
  with members:
    - "heterogeneous multilayer generalized operational perceptron"
    - "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"
    - "progressive learning for systematic design of large neural networks"
    - "neural architecture search with reinforcement learning"
    - "predicting the direction of stock market prices using random forest"

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)