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: "stochasticnet forming deep neural networks via stochastic connectivity"
  Abstract: Deep neural networks is a branch in machine learning that has seen a meteoric rise in popularity due to its powerful abilities to represent and model high-level abstractions in highly complex data. One area in deep neural networks that is ripe for exploration is neural connectivity formation. A pivotal study on the brain tissue of rats found that synaptic formation for specific functional connectivity in neocortical neural microcircuits can be surprisingly well modeled and predicted as a random formation. Motivated by this intriguing finding, we introduce the concept of StochasticNet, where deep neural networks are formed via stochastic connectivity between neurons. As a result, any type of deep neural networks can be formed as a StochasticNet by allowing the neuron connectivity to be stochastic. Stochastic synaptic formations, in a deep neural network architecture, can allow for efficient utilization of neurons for performing specific tasks. To evaluate the feasibility of such a deep neural network architecture, we train a StochasticNet using four different image datasets (CIFAR-10, MNIST, SVHN, and STL-10). Experimental results show that a StochasticNet, using less than half the number of neural connections as a conventional deep neural network, achieves comparable accuracy and reduces overfitting on the CIFAR-10, MNIST and SVHN dataset. Interestingly, StochasticNet with less than half the number of neural connections, achieved a higher accuracy (relative improvement in test error rate of ~6% compared to ConvNet) on the STL-10 dataset than a conventional deep neural network. Finally, StochasticNets have faster operational speeds while achieving better or similar accuracy performances.

Connected through hyperedge HE-1 (induced by source paper "neural networks on random graphs"):
  with members:
    - "deep connectomics networks neural network architectures inspired by neuronal networks"
    - "exploring randomly wired neural networks for image recognition"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "deep networks with stochastic depth"
    - "neural architecture search with reinforcement learning"
    - "designing neural network architectures using reinforcement learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "deep learning with long short term memory for time series prediction"):
  with members:
    - "lstm a search space odyssey"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "layer wise learning of stochastic neural networks with information bottleneck"):
  with members:
    - "opening the black box of deep neural networks via information"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "deep variational information bottleneck"
    - "towards evaluating the robustness of neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "predicting distributions with linearizing belief networks"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-4 (induced by source paper "discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection"):
  with members:
    - "evolution in groups a deeper look at synaptic cluster driven evolution of deep neural networks"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "evolutionary synthesis of deep neural networks via synaptic cluster driven genetic encoding"
    - "discovery radiomics via stochasticnet sequencers for cancer detection"

Connected through hyperedge HE-5 (induced by source paper "traffic prediction based on random connectivity in deep learning with long short term memory"):
  with members:
    - "neural decomposition of time series data for effective generalization"

Connected through hyperedge HE-6 (induced by source paper "evolutionary synthesis of deep neural networks via synaptic cluster driven genetic encoding"):
  with members:
    - "learning structured sparsity in deep neural networks"
    - "end to end learning for self driving cars"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "semantic image segmentation via deep parsing network"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "training cnns with low rank filters for efficient image classification"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-7 (induced by source paper "sparsely connected neural networks towards efficient vlsi implementation of deep neural networks"):
  with members:
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "nerd a neural response divergence approach to visual salience detection"):
  with members:
    - "matconvnet convolutional neural networks for matlab"

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.CV(Computer Vision and Pattern Recognition)