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: "heterogeneous multilayer generalized operational perceptron"
  Abstract: The traditional multilayer perceptron (MLP) using a McCulloch–Pitts neuron model is inherently limited to a set of neuronal activities, i.e., linear weighted sum followed by nonlinear thresholding step. Previously, generalized operational perceptron (GOP) was proposed to extend the conventional perceptron model by defining a diverse set of neuronal activities to imitate a generalized model of biological neurons. Together with GOP, a progressive operational perceptron (POP) algorithm was proposed to optimize a predefined template of multiple homogeneous layers in a layerwise manner. In this paper, we propose an efficient algorithm to learn a compact, fully heterogeneous multilayer network that allows each individual neuron, regardless of the layer, to have distinct characteristics. Based on the complexity of the problem, the proposed algorithm operates in a progressive manner on a neuronal level, searching for a compact topology, not only in terms of depth but also width, i.e., the number of neurons in each layer. The proposed algorithm is shown to outperform other related learning methods in extensive experiments on several classification problems.

Connected through hyperedge HE-1 (induced by source paper "subset sampling for progressive neural network learning"):
  with members:
    - "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"
    - "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 "architecture search by estimation of network structure distributions"):
  with members:
    - "operational neural networks"
    - "evaluating the search phase of neural architecture search"
    - "instanas instance aware neural architecture search"
    - "finding better topologies for deep convolutional neural networks by evolution"
    - "regularized evolution for image classifier architecture search"
    - "methods for interpreting and understanding deep neural networks"
    - "darts differentiable architecture search"
    - "understanding batch normalization"

Connected through hyperedge HE-3 (induced by source paper "operational neural networks"):
  with members:
    - "progressive operational perceptron with memory"
    - "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-4 (induced by source paper "data driven neural architecture learning for financial time series forecasting"):
  with members:
    - "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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "progressive operational perceptron with memory"):
  with members:
    - "data driven neural architecture learning for financial time series forecasting"
    - "universal approximation with quadratic deep networks"
    - "progressive learning for systematic design of large neural 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)