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: "reinforced evolutionary neural architecture search"
  Abstract: Neural architecture search (NAS) is an important yet challenging task in network design due to its high computational consumption and low stability. To address these two issues, we propose the Reinforced Evolutionary Neural Architecture Search (RENAS), which is an evolutionary method with reinforced mutation for NAS. Our method integrates reinforced mutation into an evolution algorithm for neural architecture exploration, in which a mutation controller to learn the effects of slight modifications and make mutation actions. The reinforced mutation controller instructs the model population to evolve efficiently in a suitable direction. Furthermore, as child models can inherit parameters from their parents during evolution, our method requires very limited computational resources. We conduct the proposed search method on CIFAR-10 with 4 GPUs (Titan Xp) across 1.5 days and discover a powerful network architecture. This architecture achieves a competitive result on CIFAR-10. We further apply the explored network architecture to the mobile setting ImageNet. The network achieves a new state-of-the-art accuracy, i.e., 75.7\% top-1 accuracy with 5.36M parameters.

Connected through hyperedge HE-1 (induced by source paper "one shot neural architecture search via self evaluated template network"):
  with members:
    - "network pruning via transformable architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "densely connected search space for more flexible neural architecture search"
    - "nas bench 101 towards reproducible neural architecture search"
    - "prototype propagation networks ppn for weakly supervised few shot learning on category graph"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-2 (induced by source paper "graph guided architecture search for real time semantic segmentation"):
  with members:
    - "customizable architecture search for semantic segmentation"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-3 (induced by source paper "auto gnn neural architecture search of graph neural networks"):
  with members:
    - "agan towards automated design of generative adversarial networks"
    - "exploring randomly wired neural networks for image recognition"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "graphnas graph neural architecture search with reinforcement learning"
    - "fast graph representation learning with pytorch geometric"
    - "regularized evolution for image classifier architecture search"
    - "attention models in graphs a survey"

Connected through hyperedge HE-4 (induced by source paper "scalable reinforcement learning based neural architecture search for cancer deep learning research"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "evolutionary neural automl for deep learning"
    - "evaluating the search phase of neural architecture search"
    - "balsam automated scheduling and execution of dynamic data intensive hpc workflows"
    - "evolutionary neural architecture search for image restoration"
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"

Connected through hyperedge HE-5 (induced by source paper "blockwisely supervised neural architecture search with knowledge distillation"):
  with members:
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "scarlet nas bridging the gap between stability and scalability in weight sharing neural architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "mixconv mixed depthwise convolutional kernels"
    - "improving one shot nas by suppressing the posterior fading"
    - "single path one shot neural architecture search with uniform sampling"
    - "moga searching beyond mobilenetv3"

Connected through hyperedge HE-6 (induced by source paper "multi objective reinforced evolution in mobile neural architecture search"):
  with members:
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "policy optimization with penalized point probability distance an alternative to proximal policy optimization"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "nsga net neural architecture search using multi objective genetic algorithm"
    - "nsga net a multi objective genetic algorithm for neural architecture search"

Connected through hyperedge HE-7 (induced by source paper "nasib neural architecture search within budget"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "a survey on neural architecture search"
    - "instanas instance aware neural architecture search"
    - "neural architecture construction using envelopenets"
    - "graph hypernetworks for neural architecture search"
    - "you only search once single shot neural architecture search via direct sparse optimization"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"

Connected through hyperedge HE-8 (induced by source paper "experiencethinking hyperparameter optimization with budget constraints"):
  with members:
    - "deep neural architecture search with deep graph bayesian optimization"
    - "bayesnas a bayesian approach for neural architecture search"
    - "autotune a derivative free optimization framework for hyperparameter tuning"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "xgboost a scalable tree boosting system"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"

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)