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: "speeding up the hyperparameter optimization of deep convolutional neural networks"
  Abstract: Most learning algorithms require the practitioner to manually set the values of many hyperparameters before the learning process can begin. However, with modern algorithms, the evaluation of a give...

Connected through hyperedge HE-1 (induced by source paper "a greedy constructive algorithm for the optimization of neural network architectures"):
  with members:
    - "optimizing deep neural network architecture a tabu search based approach"
    - "neural architecture optimization"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "efficient architecture search by network transformation"
    - "neural architecture search with reinforcement learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "bayesian hyperparameter optimization with botorch gpytorch and ax"):
  with members:
    - "tiered graph autoencoders with pytorch geometric for molecular graphs"
    - "botorch programmable bayesian optimization in pytorch"
    - "deep learning for molecular graphs with tiered graph autoencoders and graph classification"
    - "tiered latent representations and latent spaces for molecular graphs"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "bayesian optimization for machine learning a practical guidebook"

Connected through hyperedge HE-3 (induced by source paper "image synthesis in multi contrast mri with conditional generative adversarial networks"):
  with members:
    - "an introduction to image synthesis with generative adversarial nets"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "synthesis of positron emission tomography pet images via multi channel generative adversarial networks gans"
    - "freehand ultrasound image simulation with spatially conditioned generative adversarial networks"
    - "denoising of 3d magnetic resonance images with multi channel residual learning of convolutional neural network"
    - "virtual pet images from ct data using deep convolutional networks initial results"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "a review of meta reinforcement learning for deep neural networks architecture search"):
  with members:
    - "evolutionary architecture search for deep multitask networks"
    - "efficient progressive neural architecture search"
    - "accelerating neural architecture search using performance prediction"
    - "progressive neural architecture search"
    - "efficient architecture search by network transformation"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "deep reinforcement learning a brief survey"
    - "a genetic programming approach to designing convolutional neural network architectures"

Connected through hyperedge HE-5 (induced by source paper "gradient target propagation"):
  with members:
    - "biologically motivated algorithms for propagating local target representations"
    - "on weight initialization in deep neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "hyperparameters optimization in deep convolutional neural network bayesian approach with gaussian process prior"
    - "a theory of local learning the learning channel and the optimality of backpropagation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "how auto encoders could provide credit assignment in deep networks via target propagation"
    - "adam a method for stochastic optimization"

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)