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: "fast accurate and lightweight super resolution with neural architecture search"
  Abstract: Deep convolution neural networks demonstrate impressive results in the super-resolution domain. A series of studies concentrate on improving peak signal noise ratio (PSNR) by using much deeper layers, which are not friendly to constrained resources. Pursuing a trade-off between the restoration capacity and the simplicity of models is still non-trivial. Recent contributions are struggling to manually maximize this balance, while our work achieves the same goal automatically with neural architecture search. Specifically, we handle super-resolution with a multi-objective approach. We also propose an elastic search tactic at both micro and macro level, based on a hybrid controller that profits from evolutionary computation and reinforcement learning. Quantitative experiments help us to draw a conclusion that our generated models dominate most of the state-of-the-art methods with respect to the individual FLOPS.

Connected through hyperedge HE-1 (induced by source paper "mixpath a unified approach for one shot neural architecture search"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "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"
    - "fair darts eliminating unfair advantages in differentiable architecture search"
    - "understanding and robustifying differentiable architecture search"
    - "the evolved transformer"
    - "mixconv mixed depthwise convolutional kernels"
    - "multi objective reinforced evolution in mobile neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "hierarchical neural architecture search for single image super resolution"):
  with members:
    - "discrimination aware network pruning for deep model compression"
    - "lightweight feature fusion network for single image super resolution"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "adversarial learning with local coordinate coding"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "dual reconstruction nets for image super resolution with gradient sensitive loss"

Connected through hyperedge HE-3 (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"
    - "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"
    - "combination of hyperband and bayesian optimization for hyperparameter optimization in deep learning"

Connected through hyperedge HE-4 (induced by source paper "exploiting operation importance for differentiable neural architecture search"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "searching for mobilenetv3"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "multinomial distribution learning for effective neural architecture search"
    - "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-5 (induced by source paper "progressive differentiable architecture search bridging the depth gap between search and evaluation"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-6 (induced by source paper "lightweight image super resolution with adaptive weighted learning network"):
  with members:
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "deep learning for image super resolution a survey"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "residual convolutional neural network revisited with active weighted mapping"
    - "image super resolution using very deep residual channel attention networks"
    - "wide activation for efficient and accurate image super resolution"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"

Connected through hyperedge HE-7 (induced by source paper "a matrix in matrix neural network for image super resolution"):
  with members:
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "fast and accurate single image super resolution via information distillation network"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "residual dense network for image super resolution"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-8 (induced by source paper "understanding and improving one shot neural architecture optimization"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "the evolved transformer"
    - "single path one shot neural architecture search with uniform sampling"
    - "autoslim towards one shot architecture search for channel numbers"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"

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)