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: "the effects of hyperparameters on sgd training of neural networks"
  Abstract: The performance of neural network classifiers is determined by a number of hyperparameters, including learning rate, batch size, and depth. A number of attempts have been made to explore these parameters in the literature, and at times, to develop methods for optimizing them. However, exploration of parameter spaces has often been limited. In this note, I report the results of large scale experiments exploring these different parameters and their interactions.

Connected through hyperedge HE-1 (induced by source paper "demystifying learning rate polices for high accuracy training of deep neural networks"):
  with members:
    - "a comparative measurement study of deep learning as a service framework"
    - "super convergence very fast training of residual networks using large learning rates"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "channel pruning for accelerating very deep neural networks"
    - "sgdr stochastic gradient descent with restarts"
    - "an overview of gradient descent optimization algorithms"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "a sensitivity analysis of attention gated convolutional neural networks for sentence classification"):
  with members:
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "an attention gated convolutional neural network for sentence classification"
    - "attention is all you need"
    - "tensorflow a system for large scale machine learning"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "bayesian optimization of text representations"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"

Connected through hyperedge HE-3 (induced by source paper "measuring the effects of data parallelism on neural network training"):
  with members:
    - "introduction to online convex optimization"
    - "understanding short horizon bias in stochastic meta optimization"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "large scale distributed neural network training through online distillation"
    - "don t use large mini batches use local sgd"
    - "revisiting small batch training for deep neural networks"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "imagenet training in minutes"

Connected through hyperedge HE-4 (induced by source paper "exploring the design space of deep convolutional neural networks at large scale"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "origami a 803 gop s w convolutional network accelerator"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "theano mpi a theano based distributed training framework"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "ristretto hardware oriented approximation of convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "how to scale distributed deep learning"

Connected through hyperedge HE-5 (induced by source paper "operational calculus for differentiable programming"):
  with members:
    - "associative long short term memory"
    - "hyperparameter optimization of deep neural networks using non probabilistic rbf surrogate model"
    - "theano a python framework for fast computation of mathematical expressions"
    - "a neural algorithm of artistic style"
    - "automatic differentiation in machine learning a survey"
    - "neural programmer interpreters"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "firecaffe near linear acceleration of deep neural network training on compute clusters"):
  with members:
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep image scaling up image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "maxdnn an efficient convolution kernel for deep learning with maxwell gpus"
    - "deeplogo hitting logo recognition with the deep neural network hammer"
    - "end to end training of deep visuomotor policies"
    - "empirical evaluation of rectified activations in convolutional network"
    - "theano based large scale visual recognition with multiple gpus"

Connected through hyperedge HE-7 (induced by source paper "no more pesky learning rate guessing games"):
  with members:
    - "sgdr stochastic gradient descent with restarts"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "an empirical evaluation of deep learning on highway driving"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "equilibrated adaptive learning rates for non convex optimization"

Connected through hyperedge HE-8 (induced by source paper "cyclical learning rates for training neural networks"):
  with members:
    - "sgdr stochastic gradient descent with restarts"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "an overview of gradient descent optimization algorithms"
    - "an empirical evaluation of deep learning on highway driving"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)