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: "deep clustered convolutional kernels"
  Abstract: Deep neural networks have recently achieved state of the art performance thanks to new training algorithms for rapid parameter estimation and new regularization methods to reduce overfitting. However, in practice the network architecture has to be manually set by domain experts, generally by a costly trial and error procedure, which often accounts for a large portion of the final system performance. We view this as a limitation and propose a novel training algorithm that automatically optimizes network architecture, by progressively increasing model complexity and then eliminating model redundancy by selectively removing parameters at training time. For convolutional neural networks, our method relies on iterative split/merge clustering of convolutional kernels interleaved by stochastic gradient descent. We present a training algorithm and experimental results on three different vision tasks, showing improved performance compared to similarly sized hand-crafted architectures.

Connected through hyperedge HE-1 (induced by source paper "harmonic convolutional networks based on discrete cosine transform"):
  with members:
    - "basisconv a method for compressed representation and learning in cnns"
    - "scattering networks for hybrid representation learning"
    - "hybrid task cascade for instance segmentation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "wavelet based edge feature enhancement for convolutional neural networks"
    - "progressive neural architecture search"
    - "random erasing data augmentation"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-2 (induced by source paper "a particle swarm optimization based flexible convolutional autoencoder for image classification"):
  with members:
    - "evolving unsupervised deep neural networks for learning meaningful representations"
    - "large scale evolution of image classifiers"
    - "convolution by evolution differentiable pattern producing networks"
    - "designing neural network architectures using reinforcement learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "harmonic networks integrating spectral information into cnns"):
  with members:
    - "wavelet based edge feature enhancement for convolutional neural networks"
    - "towards image understanding from deep compression without decoding"
    - "scaling the scattering transform deep hybrid networks"
    - "wavelet convolutional neural networks for texture classification"
    - "harmonic networks deep translation and rotation equivariance"
    - "wide residual networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-4 (induced by source paper "large scale evolution of image classifiers"):
  with members:
    - "convolutional neural fabrics"
    - "deep networks with stochastic depth"
    - "planet photo geolocation with convolutional neural networks"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "neural architecture search with reinforcement learning"
    - "convolution by evolution differentiable pattern producing networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-5 (induced by source paper "evolving deep convolutional neural networks for image classification"):
  with members:
    - "large scale evolution of image classifiers"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "neural architecture search with reinforcement learning"
    - "convolution by evolution differentiable pattern producing networks"
    - "designing neural network architectures using reinforcement learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for 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.LG(Machine Learning)