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: "nsga net a multi objective genetic algorithm for neural architecture search"
  Abstract: This paper introduces NSGA-Net, an evolutionary approach for neural architecture search (NAS). NSGA-Net is designed with three goals in mind: (1) a NAS procedure for multiple, possibly conflicting, objectives, (2) efficient exploration and exploitation of the space of potential neural network architectures, and (3) output of a diverse set of network architectures spanning a trade-off frontier of the objectives in a single run. NSGA-Net is a population-based search algorithm that explores a space of potential neural network architectures in three steps, namely, a population initialization step that is based on prior-knowledge from hand-crafted architectures, an exploration step comprising crossover and mutation of architectures and finally an exploitation step that applies the entire history of evaluated neural architectures in the form of a Bayesian Network prior. Experimental results suggest that combining the objectives of minimizing both an error metric and computational complexity, as measured by FLOPS, allows NSGA-Net to find competitive neural architectures near the Pareto front of both objectives on two different tasks, object classification and object alignment. NSGA-Net obtains networks that achieve 3.72% (at 4.5 million FLOP) error on CIFAR-10 classification and 8.64% (at 26.6 million FLOP) error on the CMU-Car alignment task. Code available at: this https URL

Connected through hyperedge HE-1 (induced by source paper "population based training for loss function optimization"):
  with members:
    - "evolving loss functions with multivariate taylor polynomial parameterizations"
    - "visualising basins of attraction for the cross entropy and the squared error neural network loss functions"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "improved training speed accuracy and data utilization through loss function optimization"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "nsga net neural architecture search using multi objective genetic algorithm"

Connected through hyperedge HE-2 (induced by source paper "cars continuous evolution for efficient neural architecture search"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "progressive darts bridging the optimization gap for nas in the wild"
    - "autogan neural architecture search for generative adversarial networks"
    - "auto reid searching for a part aware convnet for person re identification"
    - "single path one shot neural architecture search with uniform sampling"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "learning sparse networks using targeted dropout"

Connected through hyperedge HE-3 (induced by source paper "automated design of error resilient and hardware efficient deep neural networks"):
  with members:
    - "understanding and robustifying differentiable architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "autodispnet improving disparity estimation with automl"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "methods for interpreting and understanding deep neural networks"
    - "path level network transformation for efficient architecture search"

Connected through hyperedge HE-4 (induced by source paper "evaluating the search phase of neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "on network design spaces for visual recognition"
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "nas bench 101 towards reproducible neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-5 (induced by source paper "architecture search by estimation of network structure distributions"):
  with members:
    - "heterogeneous multilayer generalized operational perceptron"
    - "operational neural networks"
    - "evaluating the search phase of neural architecture search"
    - "instanas instance aware neural architecture search"
    - "finding better topologies for deep convolutional neural networks by evolution"
    - "regularized evolution for image classifier architecture search"
    - "methods for interpreting and understanding deep neural networks"
    - "darts differentiable architecture search"

Connected through hyperedge HE-6 (induced by source paper "automated machine learning state of the art and open challenges"):
  with members:
    - "learning internal representations"
    - "meta learning a survey"
    - "autostacker a compositional evolutionary learning system"
    - "regularized evolution for image classifier architecture search"
    - "rafiki machine learning as an analytics service system"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"

Connected through hyperedge HE-7 (induced by source paper "fair darts eliminating unfair advantages in differentiable architecture search"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "one shot neural architecture search via self evaluated template network"
    - "searching for a robust neural architecture in four gpu hours"
    - "understanding and robustifying differentiable architecture search"
    - "searching for mobilenetv3"
    - "mixconv mixed depthwise convolutional kernels"
    - "xnas neural architecture search with expert advice"

Connected through hyperedge HE-8 (induced by source paper "fairnas rethinking evaluation fairness of weight sharing neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "understanding and robustifying differentiable architecture search"
    - "mixconv mixed depthwise convolutional kernels"
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "evaluating the search phase of neural 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)